Task1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ff0a5c77",
   "metadata": {},
   "source": [
    "    1.\n",
    "\n",
    "------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "265c886b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "import math "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "56c954ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6432627b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the first 34 values of each line and label those as the values\n",
    "ionoValues = np.genfromtxt(\"ionosphere.txt\", delimiter=\",\",usecols=np.arange(34))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4c145536",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the last value from each line and desginate it as the label of each value\n",
    "ionoLabels = np.genfromtxt(\"ionosphere.txt\", delimiter=\",\", usecols=34, dtype='int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ece837b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1\n",
      "  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1\n",
      "  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1\n",
      "  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1\n",
      "  1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1\n",
      " -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1\n",
      " -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1\n",
      " -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1\n",
      " -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1\n",
      " -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1\n",
      " -1  1 -1  1 -1  1 -1  1 -1  1 -1  1 -1  1  1  1  1  1  1  1  1  1  1  1\n",
      "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
      "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
      "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
      "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1]\n"
     ]
    }
   ],
   "source": [
    "print(ionoLabels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(351,)\n"
     ]
    }
   ],
   "source": [
    "print(ionoLabels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9687d465",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.       0.       0.99539 ... -0.54487  0.18641 -0.453  ]\n",
      " [ 1.       0.       1.      ... -0.06288 -0.13738 -0.02447]\n",
      " [ 1.       0.       1.      ... -0.2418   0.56045 -0.38238]\n",
      " ...\n",
      " [ 1.       0.       0.94701 ...  0.00442  0.92697 -0.00577]\n",
      " [ 1.       0.       0.90608 ... -0.03757  0.87403 -0.16243]\n",
      " [ 1.       0.       0.8471  ... -0.06678  0.85764 -0.06151]]\n"
     ]
    }
   ],
   "source": [
    "print(ionoValues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(351, 34)\n"
     ]
    }
   ],
   "source": [
    "print(ionoValues.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe1e1d8b",
   "metadata": {},
   "source": [
    "    2.\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8a39b62d",
   "metadata": {},
   "outputs": [],
   "source": [
    "irisX_train, irisX_test, irisY_train, irisY_test = train_test_split(iris['data'], iris['target'], random_state=2309)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "92ea27d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(112, 4) (38, 4)\n"
     ]
    }
   ],
   "source": [
    "print(irisX_train.shape,irisX_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "65ab6ce4",
   "metadata": {},
   "outputs": [],
   "source": [
    "ionoX_train, ionoX_test, ionoY_train, ionoY_test = train_test_split(ionoValues, ionoLabels, random_state=2309)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 34) (88, 34)\n"
     ]
    }
   ],
   "source": [
    "print(ionoX_train.shape, ionoX_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5671541a",
   "metadata": {},
   "source": [
    "    3. \n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89c0fe0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def distanceBetweenPoints(vector1, vector2):\n",
    "    'This function calculates/returns the distance between two vectors: vector1 and vector2'\n",
    "    # Subtracting the two given vectors\n",
    "        \n",
    "    uv = np.subtract(vector1,vector2)\n",
    "\n",
    "    # Finding the dot product of the 2\n",
    "        \n",
    "    total = np.dot(uv,uv)\n",
    "\n",
    "    #Returning the distance between the 2 vectors\n",
    "    \n",
    "    return np.sqrt(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b3d08a2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([5, 2.9, 1, 0.2])\n",
    "\n",
    "y = np.array([2, 1.3, 9, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "959c8cc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.486832980505138\n"
     ]
    }
   ],
   "source": [
    "print(distanceBetweenPoints(x,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "585615eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def nearestNeighbour(trainingSetValues, trainingSetLabels, valueToTest):\n",
    "    'This function calculates/returns the label of 1 Nearest Neighbour given: a Training dataset, its corresponding label and the value to predict a label'\n",
    "\n",
    "\n",
    "    # Set the current nearest label to be 0\n",
    "    nearestLabel = 0\n",
    "    \n",
    "    # Set the current least distance to be infinite\n",
    "    leastDistance = math.inf \n",
    "    \n",
    "    # For every value in the training set\n",
    "    for i in range(0,trainingSetValues.shape[0]):\n",
    "\n",
    "        # Calculate the distance between the given training set value and the value to test\n",
    "        distanceBetween = distanceBetweenPoints(trainingSetValues[i], valueToTest)\n",
    "    \n",
    "        # If the distance calculated is less than the leastDistance then update the distance and the labels\n",
    "        if distanceBetween < leastDistance:\n",
    "            leastDistance = distanceBetween\n",
    "            nearestLabel = trainingSetLabels[i]\n",
    "    \n",
    "    return nearestLabel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "19039337",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculateSuccessRate (trainingSetValues, trainingSetLabels, testSetValues, testSetLabels):\n",
    "    'This function calculates the error rate of the 1 Nearest Neighbours algorithm, given: The Training Dataset, its corresponding labels, dataset to test, corresponding labels to test data'\n",
    "\n",
    "    # Counter variable to count the mistakes made by the program\n",
    "    errorCount = 0\n",
    "\n",
    "    predictedLabels =[]\n",
    "\n",
    "    # For every value in the test set\n",
    "    for i in range(0, len(testSetValues)):\n",
    "       \n",
    "       # Append the predicted labels to the new array\n",
    "       predictedLabels.append(nearestNeighbour(trainingSetValues,trainingSetLabels, testSetValues[i]))\n",
    "\n",
    "       print(\"Predicted Label: \", predictedLabels[i])\n",
    "       print(\"Actual Label: \", testSetLabels[i])\n",
    "       print(\" \")\n",
    "        \n",
    "       # If the predicted does not match the correct one, add 1 to the error counter\n",
    "       if predictedLabels[i] != testSetLabels[i]:\n",
    "           errorCount +=1\n",
    "\n",
    "    # Returns the Success rate in percentage\n",
    "    return ((1- (errorCount/testSetLabels.shape[0])) * 100)\n",
    "\n",
    "def calculateErrorRate(successRate):\n",
    "    # Returns the error rate in percentage\n",
    "    return 100 - successRate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bef4c927",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Label:  1\n",
      "Actual Label:  -1\n",
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      "Predicted Label:  -1\n",
      "Actual Label:  -1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n"
     ]
    }
   ],
   "source": [
    "successIono = calculateSuccessRate(ionoX_train,ionoY_train, ionoX_test, ionoY_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f89254be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
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      "Predicted Label:  2\n",
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      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  2\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  2\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  2\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  2\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n"
     ]
    }
   ],
   "source": [
    "successIris = calculateSuccessRate(irisX_train,irisY_train, irisX_test,irisY_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "01b0ffa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error rate for 1 NN for Ionosphere = 12.5%\n"
     ]
    }
   ],
   "source": [
    "print(\"Error rate for 1 NN for Ionosphere =\", str(calculateErrorRate(successIono))+ \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fc64b541",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success rate for 1 NN for Ionosphere = 87.5%\n"
     ]
    }
   ],
   "source": [
    "print(\"Success rate for 1 NN for Ionosphere =\", str(successIono)+ \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "35106721",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error rate for 1 NN for Iris = 7.89473684210526%\n"
     ]
    }
   ],
   "source": [
    "print(\"Error rate for 1 NN for Iris =\", str(calculateErrorRate(successIris))+ \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c750b0b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success rate for 1 NN for Iris = 92.10526315789474%\n"
     ]
    }
   ],
   "source": [
    "print(\"Success rate for 1 NN for Iris =\", str(successIris) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c541d207",
   "metadata": {},
   "source": [
    "    3 (Nearest Neighbour N).\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "892b2638",
   "metadata": {},
   "outputs": [],
   "source": [
    "def neighboursWithN(trainingSetValues, trainingSetLabels, valueToTest, numberOfNeighbours):\n",
    "    'This function calculates N Nearest Neighbours labels, given: The Training Dataset, its corresponding labels, value to test, number of neighbours wanted'\n",
    "\n",
    "    nearestNeighbors = {}  # A dictionary to store nearest neighbors and distances\n",
    "\n",
    "    # For every value in the training data set\n",
    "    for i in range(trainingSetValues.shape[0]):\n",
    "\n",
    "        # Calculate the distance between the training set value and the value we are testing\n",
    "        distanceBetween = distanceBetweenPoints(trainingSetValues[i], valueToTest)\n",
    "\n",
    "        # Check length of the dictionary to see if it is less than the number of NN wanted to add to dictionary\n",
    "        # And checks if the current distance is smaller than the largest distance in the neighbors (max size in array is 4)\n",
    "        if len(nearestNeighbors) < numberOfNeighbours or distanceBetween < max(nearestNeighbors.values()):\n",
    "\n",
    "            # If it is, add this new distance to the dictionary\n",
    "            nearestNeighbors[i] = distanceBetween\n",
    "\n",
    "        # If the length of the dictionary is greater than the number of Nearest Neighbours\n",
    "        if len(nearestNeighbors) > numberOfNeighbours:\n",
    "\n",
    "            # Get the largest distance found in the dictionary\n",
    "            farthest_neighbor = max(nearestNeighbors, key=nearestNeighbors.get)\n",
    "\n",
    "            # Delete the furthest neighbour from the dictionary\n",
    "            del nearestNeighbors[farthest_neighbor]\n",
    "\n",
    "\n",
    "\n",
    "    # Get the labels of the nearest neighbors based on the i-th value stored in the dictionary\n",
    "    # It will iterate through all the labels till the i-th labels are reached according to the dictionary keys\n",
    "    nearestLabels = [trainingSetLabels[n] for n in nearestNeighbors.keys()]\n",
    "\n",
    "    # Returns the array of all the nearest labels\n",
    "    return nearestLabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "381d195e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def nearestNeighbourN(trainingSetValues, trainingSetLabels, valueToTest, numberOfNeighbours):\n",
    "    'This function calculates N Nearest Neighbours predicted label, given: The Training Dataset, its corresponding labels, value to test, number of neighbours wanted'\n",
    "\n",
    "    # Get all the nearest neighbours' labels\n",
    "    nearest_labels = neighboursWithN(trainingSetValues, trainingSetLabels, valueToTest, numberOfNeighbours)\n",
    "\n",
    "    # Create a dictionary with \"Label: Number of occurences in array\"\n",
    "    label_counts = {label: nearest_labels.count(label) for label in set(nearest_labels)}   \n",
    "     \n",
    "    # Returns the key with the max value in the dictionary\n",
    "        # list(label_counts.keys()) = Gets the all the labels from the dictionary\n",
    "        # list(label_counts.values()) = Gets the values from those keys\n",
    "        # .index(max(label_counts.values())) = Gets the index value of the max item\n",
    "     \n",
    "    return list(label_counts.keys())[list(label_counts.values()).index(max(label_counts.values()))]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ca23142a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculateSuccessRateWithN(trainingSetValues, trainingSetLabels, testSetValues, testSetLabels, numberOfNeighbours):\n",
    "\n",
    "    errorCount = 0\n",
    "    predictedLabels =[]\n",
    "\n",
    "    for i in range(0, len(testSetValues)):\n",
    "       predictedLabels.append(nearestNeighbourN(trainingSetValues,trainingSetLabels, testSetValues[i],numberOfNeighbours))\n",
    "\n",
    "       print(\"Predicted Label: \", predictedLabels[i])\n",
    "       print(\"Actual Label: \", testSetLabels[i])\n",
    "       print(\" \")\n",
    "\n",
    "       if predictedLabels[i] != testSetLabels[i]:\n",
    "           errorCount +=1\n",
    "\n",
    "    return ((1- (errorCount/testSetLabels.shape[0])) * 100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d33005dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Label:  1\n",
      "Actual Label:  -1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  -1\n",
      " \n",
      "Predicted Label:  -1\n",
      "Actual Label:  -1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  -1\n",
      " \n",
      "Predicted Label:  -1\n",
      "Actual Label:  -1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  -1\n",
      " \n",
      "Predicted Label:  -1\n",
      "Actual Label:  -1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  -1\n",
      "Actual Label:  -1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  1\n",
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      "Predicted Label:  1\n",
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      "Predicted Label:  -1\n",
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      "Predicted Label:  1\n",
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      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  -1\n",
      "Actual Label:  -1\n",
      " \n",
      "Predicted Label:  -1\n",
      "Actual Label:  -1\n",
      " \n",
      "Predicted Label:  -1\n",
      "Actual Label:  -1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  -1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  -1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  -1\n",
      "Actual Label:  -1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n"
     ]
    }
   ],
   "source": [
    "successIono3NN = calculateSuccessRateWithN(ionoX_train,ionoY_train, ionoX_test, ionoY_test,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8f83a61a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  2\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  2\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  2\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  2\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  2\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  2\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  2\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  2\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  2\n",
      " \n",
      "Predicted Label:  0\n",
      "Actual Label:  0\n",
      " \n",
      "Predicted Label:  2\n",
      "Actual Label:  2\n",
      " \n",
      "Predicted Label:  1\n",
      "Actual Label:  1\n",
      " \n"
     ]
    }
   ],
   "source": [
    "successIris3NN = calculateSuccessRateWithN(irisX_train,irisY_train, irisX_test,irisY_test,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ec54fbd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success rate for 3 NN Iris = 92.10526315789474%\n"
     ]
    }
   ],
   "source": [
    "print(\"Success rate for 3 NN Iris =\", str(successIris3NN)+ \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "37209fff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success rate for 3 NN Ionosphere = 86.36363636363636%\n"
     ]
    }
   ],
   "source": [
    "print(\"Success rate for 3 NN Ionosphere =\", str(successIono3NN)+ \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "89a3e076",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error rate for 3 NN Iris = 7.89473684210526%\n"
     ]
    }
   ],
   "source": [
    "print(\"Error rate for 3 NN Iris =\", str(calculateErrorRate(successIris3NN))+ \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f7febcc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error rate for 3 NN Ionosphere = 13.63636363636364%\n"
     ]
    }
   ],
   "source": [
    "print(\"Error rate for 3 NN Ionosphere =\", str(calculateErrorRate(successIono3NN))+ \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa6666d",
   "metadata": {},
   "source": [
    "    4.\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0dcba2ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def nonConformityMeasure(trainingSetValues, trainingSetLabels, testValue, predictedLabel):\n",
    "    nearestSameLabelD = math.inf\n",
    "    nearestOtherLabelD = math.inf\n",
    "\n",
    "    # For every item in the dataset\n",
    "    for i in range(trainingSetValues.shape[0]):\n",
    "\n",
    "        # Checks that the testValue and the training set values are not the same\n",
    "        if np.array_equal(trainingSetValues[i], testValue):\n",
    "            continue\n",
    "\n",
    "        # Calculate the distance between the current item and the item to test\n",
    "        distanceBetween = distanceBetweenPoints(trainingSetValues[i], testValue)\n",
    "\n",
    "        \n",
    "        # Checks if the current trainingValue has the same label as the testValue and the distance is less than previously calculated\n",
    "        if trainingSetLabels[i] == predictedLabel and distanceBetween < nearestSameLabelD:\n",
    "            nearestSameLabelD = distanceBetween\n",
    "\n",
    "        # Checks if the current trainingValue does not have the same label as the testValue and the distance is less than previously calculated\n",
    "        if trainingSetLabels[i] != predictedLabel and distanceBetween < nearestOtherLabelD:\n",
    "            nearestOtherLabelD = distanceBetween\n",
    "            \n",
    "\n",
    "    # Returns the non-conformity value from the equation:\n",
    "        # the distance to the nearest sample of a different class/ the distance to the nearest sample of the same class\n",
    "    return nearestOtherLabelD/nearestSameLabelD\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a42a9c32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.1 3.3 1.7 0.5]\n"
     ]
    }
   ],
   "source": [
    "print(irisX_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "18112913",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(irisY_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "04a95260",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.199078272499182\n"
     ]
    }
   ],
   "source": [
    "# Calculating actual conformity score with true label\n",
    "print(nonConformityMeasure(irisX_train,irisY_train, irisX_train[0], irisY_train[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dab1903f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0838338383339281\n"
     ]
    }
   ],
   "source": [
    "# Calculating actual conformity score with false label\n",
    "print(nonConformityMeasure(irisX_train,irisY_train, irisX_train[0], irisY_train[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2fe96330",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.16131430448882622\n"
     ]
    }
   ],
   "source": [
    "# Calculating actual conformity score with false label\n",
    "print(nonConformityMeasure(irisX_train,irisY_train, irisX_train[0], irisY_train[10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "072f009f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def nonConformityMeasureArray(trainingSetValues, trainingSetLabels):\n",
    "    'This function, given a Training Set Values and Labels, returns an array of all non-conformity scores of each training value'\n",
    "    nonConformityValues =[]\n",
    "    \n",
    "    for i in range(0,trainingSetValues.shape[0]):\n",
    "        nonConformityValues.append(nonConformityMeasure(trainingSetValues, trainingSetLabels, trainingSetValues[i], trainingSetLabels[i]))\n",
    "\n",
    "    return nonConformityValues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8161600c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.199078272499182, 2.2267315359812323, 2.6457513110645854, 1.8973665961010249, 1.4719601443879755, 2.2360679774997894, 6.462911941936913, 1.7320508075688759, 3.937003937005917, 3.6689969285267146, 6.3639610306789205, 2.9760952365713798, 2.9769227342202287, 6.359594676112968, 8.06225774829854, 6.59545297913644, 5.555505050275485, 8.567379996241574, 4.499999999999995, 1.9663841605003505, 4.415880433163935, 3.7859388972001806, 7.745966692414826, 9.528903399657278, 7.8102496759066655, 3.2403703492039275, 15.508062419270852, 6.964194138592057, 13.52774925846867, 8.660254037844396, 8.62167810425171, 9.230384607371471, 3.291402943021915, 8.602325267042618, 12.30176139149731, 3.2403703492039213, 10.53565375285273, 6.78232998312527, 4.222953153106638, 8.346656017032613, 13.152946437965893, 2.4083189157584632, 3.511884584284245, 2.701851217221262, 1.4392458342578487, 14.335271186831438, 2.1679483388678804, 0.9636241116594312, 14.474114826130117, 2.260776661041754, 10.816653826391956, 1.4005493427717781, 0.7211102550927968, 5.477225575051642, 3.0822070014844845, 5.106556871016233, 2.6457513110645903, 10.677078252031286, 8.06225774829854, 6.971370023173353, 1.974841765813151, 1.843908891458575, 2.730994934695657, 2.2156468376279874, 6.201172909582952, 2.3979157616563573, 4.509249752822893, 1.683250823060346, 2.5385910352879675, 2.2990681342044414, 5.437961803049787, 0.835939699214538, 2.7446918466985193, 4.387482193696056, 11.37248140615466, 1.3771534860493682, 2.2181073012818837, 4.135214625627065, 4.203173404306169, 2.408318915758458, 5.0990195135927685, 3.741657386773938, 3.2145502536643167, 9.949874371066214, 2.263846284534352, 4.5825756949558425, 2.9999999999999996, 1.3038404810405302, 2.999999999999998, 2.923260943784277, 2.738612787525829, 9.574271077563374, 1.6072751268321601, 14.265342617687091, 13.304134695650038, 2.5819888974716068, 15.149257407543145, 2.179449471770339, 2.7759471091757444, 2.329929490042869, 14.781745499094505, 2.6710163530403475, 5.464168999687223, 6.3101649596648315, 9.84885780179612, 5.958187643906484, 5.042675027063648, 3.550653863324734, 2.6457513110645903, 13.546217184144053, 3.0822070014844782, 3.240370349203927]\n"
     ]
    }
   ],
   "source": [
    "print(nonConformityMeasureArray(irisX_train,irisY_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3c1afc61",
   "metadata": {},
   "outputs": [],
   "source": [
    "def nonConformalPrediction(trainingSetValues, trainingSetLabels, testValue):\n",
    "    'This function, given the Training set values and corresponding labels as well as a value to test will return the non-conformal values for all the values'\n",
    "    scores =[] \n",
    "    \n",
    "    # Here I am creating a copy of the original training set as I will append the test value and all the new labels to test, iteratively to the test trainingSet\n",
    "    originalTrainingSet = np.copy(trainingSetValues)\n",
    "    originalTrainingSetLabels = np.copy(trainingSetLabels)\n",
    "\n",
    "\n",
    "    # Adding the new value to test to the training set and changing the shape to accomodate the new value\n",
    "    trainingSetValues = np.append(trainingSetValues, testValue)\n",
    "    trainingSetValues = np.reshape(trainingSetValues,(originalTrainingSet.shape[0]+1,originalTrainingSet.shape[1]))\n",
    "\n",
    "    # Count the number of labels in this training set \n",
    "    labelsToUse = countLabels(trainingSetLabels)\n",
    "\n",
    "    # For every label, add that new testValue and a given label and calculate the non-conformity value\n",
    "    # Add score to an array, clean the trainingSetLabels and move to the next label\n",
    "    for i in range(0, len(labelsToUse)):\n",
    "    \n",
    "        trainingSetLabels = np.append(trainingSetLabels, labelsToUse.pop())    \n",
    "        conformityArray = nonConformityMeasureArray(trainingSetValues, trainingSetLabels)\n",
    "        scores.append(conformityArray)\n",
    "        trainingSetLabels = np.copy(originalTrainingSetLabels)\n",
    "\n",
    "\n",
    "    # Revert the datasets to what they were previously\n",
    "    trainingSetValues = np.copy(originalTrainingSet)\n",
    "    trainingSetLabels = np.copy(originalTrainingSetLabels)\n",
    "\n",
    "    # Return an array of arrays with n dimensions where N is the number of labels\n",
    "    return scores\n",
    "\n",
    "\n",
    "\n",
    "def countLabels(trainingSetLabels):\n",
    "    'This function, given a set of labels, will produce a set containing all the distinct labels'\n",
    "    setLabels = set()\n",
    "\n",
    "    for x in range(len(trainingSetLabels)-1):\n",
    "        setLabels.add(trainingSetLabels[x])\n",
    "\n",
    "    return setLabels\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "aed94f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculatePValue(trainingSetValues, trainingSetLabels, testValue, testLabel):\n",
    "    'This function, given the training set values and labels, and a test value and its label, will calculate the P value for each data entry'\n",
    "    pValues = []\n",
    "\n",
    "    # Get the conformity scores arrays\n",
    "        # Arrays will be conformalArrays[0] = First Label Conformity Values\n",
    "    conformalArrays = nonConformalPrediction(trainingSetValues,trainingSetLabels, testValue)\n",
    "\n",
    "    labels = countLabels(trainingSetLabels)\n",
    "\n",
    "    for i in range(len(labels)):\n",
    "\n",
    "        # Setting count to 1 as lowest p(y) is 1/number of values, not 0/number of values\n",
    "        count = 1\n",
    "\n",
    "        # Produce a rank for each value based on their conformity score\n",
    "        sortedArray = np.array(conformalArrays[i]).argsort()\n",
    "\n",
    "        # This for loop will calculate the rank of the value based on their position in the sortedArray\n",
    "        for x in range(len(sortedArray)):\n",
    "\n",
    "            # If value found\n",
    "            if sortedArray[x] == len(conformalArrays[i])-1:\n",
    "                break\n",
    "\n",
    "            # Add 1 to counter\n",
    "            else:\n",
    "                count = count + 1\n",
    "        \n",
    "\n",
    "        # Calculating the P value\n",
    "        pValue = (count/len(sortedArray))\n",
    "\n",
    "        currentLabel = labels.pop()\n",
    "\n",
    "        # This if statement, makes sure that the correct Label gives a higher P value\n",
    "            # This is because the wrong label may give a higher P value but we want to make sure that we count the false P values\n",
    "        if testLabel == currentLabel:\n",
    "            pValues.append(pValue +1)\n",
    "\n",
    "        else:\n",
    "            pValues.append(pValue)\n",
    "\n",
    "        print(\"P Value for \", testValue, \" and label \", currentLabel, \" is: \",pValue)\n",
    "\n",
    "\n",
    "    return pValues\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "57f264ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P Value for  [5.5 4.2 1.4 0.2]  and label  0  is:  0.7433628318584071\n",
      "P Value for  [5.5 4.2 1.4 0.2]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [5.5 4.2 1.4 0.2]  and label  2  is:  0.008849557522123894\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.7433628318584071, 0.008849557522123894, 0.008849557522123894]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculatePValue(irisX_train,irisY_train, irisX_test[2],irisY_test[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "71898bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculateAvgFalseP(trainingSetValues, trainingSetLabels, testSetValues, testSetLabels):\n",
    "    'This function, given the training set values and labels, the test values and their labels, will calculate average false P value for the datasets'\n",
    "    falsePValues= []\n",
    "\n",
    "    for i in range(0, testSetValues.shape[0]):\n",
    "\n",
    "        # Calculates the P value for a given value and label\n",
    "        pValues = calculatePValue(trainingSetValues, trainingSetLabels, testSetValues[i],testSetLabels[i])\n",
    "        print(\" \")\n",
    "        \n",
    "        # Removes the max value, i.e the correct label P value\n",
    "        maxP = max(pValues)\n",
    "        pValues.remove(maxP)\n",
    "\n",
    "        # Appends the false values to an array\n",
    "        falsePValues.append(pValues)\n",
    "\n",
    "    cumSum = 0\n",
    "    counter = 0\n",
    "\n",
    "    # For every value in the false P values array, sum all the values \n",
    "    for x in range(len(falsePValues)):\n",
    "        for z in range(len(falsePValues[x])):\n",
    "            cumSum =  cumSum + falsePValues[x][z]\n",
    "            counter = counter + 1\n",
    "\n",
    "    # Return average false P-Value\n",
    "    return cumSum/counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c216a20d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P Value for  [5.  3.4 1.6 0.4]  and label  0  is:  0.8230088495575221\n",
      "P Value for  [5.  3.4 1.6 0.4]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [5.  3.4 1.6 0.4]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [7.  3.2 4.7 1.4]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [7.  3.2 4.7 1.4]  and label  1  is:  0.46017699115044247\n",
      "P Value for  [7.  3.2 4.7 1.4]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [5.5 4.2 1.4 0.2]  and label  0  is:  0.7433628318584071\n",
      "P Value for  [5.5 4.2 1.4 0.2]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [5.5 4.2 1.4 0.2]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [4.8 3.4 1.9 0.2]  and label  0  is:  0.6460176991150443\n",
      "P Value for  [4.8 3.4 1.9 0.2]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [4.8 3.4 1.9 0.2]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [5.  3.2 1.2 0.2]  and label  0  is:  0.7433628318584071\n",
      "P Value for  [5.  3.2 1.2 0.2]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [5.  3.2 1.2 0.2]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [6.6 3.  4.4 1.4]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [6.6 3.  4.4 1.4]  and label  1  is:  0.6017699115044248\n",
      "P Value for  [6.6 3.  4.4 1.4]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [4.8 3.  1.4 0.1]  and label  0  is:  0.9469026548672567\n",
      "P Value for  [4.8 3.  1.4 0.1]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [4.8 3.  1.4 0.1]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [6.5 2.8 4.6 1.5]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [6.5 2.8 4.6 1.5]  and label  1  is:  0.12389380530973451\n",
      "P Value for  [6.5 2.8 4.6 1.5]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [5.  3.3 1.4 0.2]  and label  0  is:  0.9469026548672567\n",
      "P Value for  [5.  3.3 1.4 0.2]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [5.  3.3 1.4 0.2]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [5.1 3.8 1.5 0.3]  and label  0  is:  0.9823008849557522\n",
      "P Value for  [5.1 3.8 1.5 0.3]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [5.1 3.8 1.5 0.3]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [6.7 2.5 5.8 1.8]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [6.7 2.5 5.8 1.8]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [6.7 2.5 5.8 1.8]  and label  2  is:  0.09734513274336283\n",
      " \n",
      "P Value for  [5.9 3.  5.1 1.8]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [5.9 3.  5.1 1.8]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [5.9 3.  5.1 1.8]  and label  2  is:  0.1415929203539823\n",
      " \n",
      "P Value for  [6.9 3.2 5.7 2.3]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [6.9 3.2 5.7 2.3]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [6.9 3.2 5.7 2.3]  and label  2  is:  0.4778761061946903\n",
      " \n",
      "P Value for  [4.8 3.1 1.6 0.2]  and label  0  is:  0.9026548672566371\n",
      "P Value for  [4.8 3.1 1.6 0.2]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [4.8 3.1 1.6 0.2]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [6.2 2.8 4.8 1.8]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [6.2 2.8 4.8 1.8]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [6.2 2.8 4.8 1.8]  and label  2  is:  0.25663716814159293\n",
      " \n",
      "P Value for  [6.  2.7 5.1 1.6]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [6.  2.7 5.1 1.6]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [6.  2.7 5.1 1.6]  and label  2  is:  0.035398230088495575\n",
      " \n",
      "P Value for  [7.9 3.8 6.4 2. ]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [7.9 3.8 6.4 2. ]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [7.9 3.8 6.4 2. ]  and label  2  is:  0.5752212389380531\n",
      " \n",
      "P Value for  [6.  2.2 5.  1.5]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [6.  2.2 5.  1.5]  and label  1  is:  0.04424778761061947\n",
      "P Value for  [6.  2.2 5.  1.5]  and label  2  is:  0.017699115044247787\n",
      " \n",
      "P Value for  [5.1 3.5 1.4 0.3]  and label  0  is:  0.9911504424778761\n",
      "P Value for  [5.1 3.5 1.4 0.3]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [5.1 3.5 1.4 0.3]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [5.1 3.4 1.5 0.2]  and label  0  is:  0.9911504424778761\n",
      "P Value for  [5.1 3.4 1.5 0.2]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [5.1 3.4 1.5 0.2]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [5.4 3.  4.5 1.5]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [5.4 3.  4.5 1.5]  and label  1  is:  0.09734513274336283\n",
      "P Value for  [5.4 3.  4.5 1.5]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [4.4 2.9 1.4 0.2]  and label  0  is:  0.9380530973451328\n",
      "P Value for  [4.4 2.9 1.4 0.2]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [4.4 2.9 1.4 0.2]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [6.3 2.3 4.4 1.3]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [6.3 2.3 4.4 1.3]  and label  1  is:  0.39823008849557523\n",
      "P Value for  [6.3 2.3 4.4 1.3]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [6.8 2.8 4.8 1.4]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [6.8 2.8 4.8 1.4]  and label  1  is:  0.11504424778761062\n",
      "P Value for  [6.8 2.8 4.8 1.4]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [4.9 3.1 1.5 0.1]  and label  0  is:  1.0\n",
      "P Value for  [4.9 3.1 1.5 0.1]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [4.9 3.1 1.5 0.1]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [5.  3.  1.6 0.2]  and label  0  is:  0.8672566371681416\n",
      "P Value for  [5.  3.  1.6 0.2]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [5.  3.  1.6 0.2]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [4.7 3.2 1.3 0.2]  and label  0  is:  0.9734513274336283\n",
      "P Value for  [4.7 3.2 1.3 0.2]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [4.7 3.2 1.3 0.2]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [5.7 4.4 1.5 0.4]  and label  0  is:  0.584070796460177\n",
      "P Value for  [5.7 4.4 1.5 0.4]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [5.7 4.4 1.5 0.4]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [5.9 3.2 4.8 1.8]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [5.9 3.2 4.8 1.8]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [5.9 3.2 4.8 1.8]  and label  2  is:  0.12389380530973451\n",
      " \n",
      "P Value for  [6.4 2.9 4.3 1.3]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [6.4 2.9 4.3 1.3]  and label  1  is:  0.5132743362831859\n",
      "P Value for  [6.4 2.9 4.3 1.3]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [6.8 3.2 5.9 2.3]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [6.8 3.2 5.9 2.3]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [6.8 3.2 5.9 2.3]  and label  2  is:  0.46017699115044247\n",
      " \n",
      "P Value for  [5.6 3.  4.5 1.5]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [5.6 3.  4.5 1.5]  and label  1  is:  0.12389380530973451\n",
      "P Value for  [5.6 3.  4.5 1.5]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [5.4 3.7 1.5 0.2]  and label  0  is:  1.0\n",
      "P Value for  [5.4 3.7 1.5 0.2]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [5.4 3.7 1.5 0.2]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [6.5 3.  5.5 1.8]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [6.5 3.  5.5 1.8]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [6.5 3.  5.5 1.8]  and label  2  is:  0.17699115044247787\n",
      " \n",
      "P Value for  [6.4 3.1 5.5 1.8]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [6.4 3.1 5.5 1.8]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [6.4 3.1 5.5 1.8]  and label  2  is:  0.24778761061946902\n",
      " \n",
      "P Value for  [5.4 3.4 1.7 0.2]  and label  0  is:  0.6991150442477876\n",
      "P Value for  [5.4 3.4 1.7 0.2]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [5.4 3.4 1.7 0.2]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "P Value for  [7.7 2.8 6.7 2. ]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [7.7 2.8 6.7 2. ]  and label  1  is:  0.008849557522123894\n",
      "P Value for  [7.7 2.8 6.7 2. ]  and label  2  is:  0.7433628318584071\n",
      " \n",
      "P Value for  [5.6 2.5 3.9 1.1]  and label  0  is:  0.008849557522123894\n",
      "P Value for  [5.6 2.5 3.9 1.1]  and label  1  is:  0.6814159292035398\n",
      "P Value for  [5.6 2.5 3.9 1.1]  and label  2  is:  0.008849557522123894\n",
      " \n",
      "The Average False P-Value of Iris is:  0.011178388448998614\n"
     ]
    }
   ],
   "source": [
    "print(\"The Average False P-Value of Iris is: \" ,calculateAvgFalseP(irisX_train,irisY_train, irisX_test,irisY_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cd0dd7ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P Value for  [ 1.       0.       1.      -0.05529  1.      -1.       0.5     -0.11111\n",
      "  0.36111 -0.22222  1.      -0.25712  0.16667 -0.11111  1.      -0.3466\n",
      "  1.      -0.38853  1.      -0.42862  0.      -0.25     1.      -0.50333\n",
      "  1.      -0.27778  1.      -0.57092  1.      -0.27778  1.      -0.63156\n",
      "  1.      -0.65935]  and label  1  is:  0.36363636363636365\n",
      "P Value for  [ 1.       0.       1.      -0.05529  1.      -1.       0.5     -0.11111\n",
      "  0.36111 -0.22222  1.      -0.25712  0.16667 -0.11111  1.      -0.3466\n",
      "  1.      -0.38853  1.      -0.42862  0.      -0.25     1.      -0.50333\n",
      "  1.      -0.27778  1.      -0.57092  1.      -0.27778  1.      -0.63156\n",
      "  1.      -0.65935]  and label  -1  is:  0.03787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.43636 -0.12727  0.58182 -0.14545  0.18182 -0.67273\n",
      "  0.34545 -0.03636  0.29091 -0.05455  0.29091  0.29091  0.36364 -0.41818\n",
      "  0.2     -0.01818  0.36364  0.05455  0.12727  0.49091  0.61818  0.16364\n",
      "  0.32727  0.16364  0.41098 -0.07027  0.34545 -0.05455  0.12727 -0.36364\n",
      "  0.29091 -0.29091]  and label  1  is:  0.4166666666666667\n",
      "P Value for  [ 1.       0.       0.43636 -0.12727  0.58182 -0.14545  0.18182 -0.67273\n",
      "  0.34545 -0.03636  0.29091 -0.05455  0.29091  0.29091  0.36364 -0.41818\n",
      "  0.2     -0.01818  0.36364  0.05455  0.12727  0.49091  0.61818  0.16364\n",
      "  0.32727  0.16364  0.41098 -0.07027  0.34545 -0.05455  0.12727 -0.36364\n",
      "  0.29091 -0.29091]  and label  -1  is:  0.022727272727272728\n",
      " \n",
      "P Value for  [ 1.       0.       1.       1.       0.       0.       0.       0.\n",
      "  0.       0.       0.47744 -0.89098 -0.51504  0.45489 -0.95489  0.28571\n",
      "  0.64662  1.       0.       0.       0.       0.       0.6203   0.20301\n",
      " -1.      -1.       1.      -1.       1.       1.       0.       0.\n",
      "  0.       0.     ]  and label  1  is:  0.06060606060606061\n",
      "P Value for  [ 1.       0.       1.       1.       0.       0.       0.       0.\n",
      "  0.       0.       0.47744 -0.89098 -0.51504  0.45489 -0.95489  0.28571\n",
      "  0.64662  1.       0.       0.       0.       0.       0.6203   0.20301\n",
      " -1.      -1.       1.      -1.       1.       1.       0.       0.\n",
      "  0.       0.     ]  and label  -1  is:  0.2803030303030303\n",
      " \n",
      "P Value for  [ 1.0000e+00  0.0000e+00  7.1521e-01 -6.4700e-03  6.6667e-01 -4.2070e-02\n",
      "  6.3107e-01 -5.1780e-02  7.7994e-01  8.0910e-02  6.7314e-01  9.7090e-02\n",
      "  6.4725e-01  1.5858e-01  6.0194e-01 -1.9420e-02  5.4369e-01 -4.5310e-02\n",
      "  4.6926e-01 -1.0032e-01  6.4725e-01  1.4887e-01  3.9159e-01  2.1683e-01\n",
      "  5.2427e-01 -5.5020e-02  4.5105e-01  4.0000e-04  3.1392e-01 -6.7960e-02\n",
      "  4.9191e-01 -1.0680e-01  3.0421e-01 -5.1780e-02]  and label  1  is:  0.4431818181818182\n",
      "P Value for  [ 1.0000e+00  0.0000e+00  7.1521e-01 -6.4700e-03  6.6667e-01 -4.2070e-02\n",
      "  6.3107e-01 -5.1780e-02  7.7994e-01  8.0910e-02  6.7314e-01  9.7090e-02\n",
      "  6.4725e-01  1.5858e-01  6.0194e-01 -1.9420e-02  5.4369e-01 -4.5310e-02\n",
      "  4.6926e-01 -1.0032e-01  6.4725e-01  1.4887e-01  3.9159e-01  2.1683e-01\n",
      "  5.2427e-01 -5.5020e-02  4.5105e-01  4.0000e-04  3.1392e-01 -6.7960e-02\n",
      "  4.9191e-01 -1.0680e-01  3.0421e-01 -5.1780e-02]  and label  -1  is:  0.011363636363636364\n",
      " \n",
      "P Value for  [ 1.       0.       0.69444  0.38889  0.       0.      -0.32937  0.69841\n",
      "  0.       0.       0.       0.       0.       0.       0.20635 -0.24206\n",
      "  0.21032  0.19444  0.46429  0.78175  0.       0.       0.       0.\n",
      "  0.73413  0.27381  0.7619   0.63492  0.       0.       0.       0.\n",
      "  0.       0.     ]  and label  1  is:  0.19318181818181818\n",
      "P Value for  [ 1.       0.       0.69444  0.38889  0.       0.      -0.32937  0.69841\n",
      "  0.       0.       0.       0.       0.       0.       0.20635 -0.24206\n",
      "  0.21032  0.19444  0.46429  0.78175  0.       0.       0.       0.\n",
      "  0.73413  0.27381  0.7619   0.63492  0.       0.       0.       0.\n",
      "  0.       0.     ]  and label  -1  is:  0.09090909090909091\n",
      " \n",
      "P Value for  [ 1.       0.       0.06404 -0.15271 -0.04433  0.05911  0.08374 -0.02463\n",
      " -0.01478  0.18719  0.06404  0.       0.12315 -0.09852  0.05911  0.\n",
      "  0.0197  -0.02956 -0.12808 -0.2069   0.06897  0.01478  0.06897  0.02956\n",
      "  0.07882  0.16256  0.28079 -0.04926 -0.05911 -0.0936   0.04433  0.05419\n",
      "  0.07389 -0.10837]  and label  1  is:  0.030303030303030304\n",
      "P Value for  [ 1.       0.       0.06404 -0.15271 -0.04433  0.05911  0.08374 -0.02463\n",
      " -0.01478  0.18719  0.06404  0.       0.12315 -0.09852  0.05911  0.\n",
      "  0.0197  -0.02956 -0.12808 -0.2069   0.06897  0.01478  0.06897  0.02956\n",
      "  0.07882  0.16256  0.28079 -0.04926 -0.05911 -0.0936   0.04433  0.05419\n",
      "  0.07389 -0.10837]  and label  -1  is:  0.3712121212121212\n",
      " \n",
      "P Value for  [1.      0.      0.7479  0.0084  0.83312 0.01659 0.82638 0.02469 0.86555\n",
      " 0.01681 0.60504 0.05882 0.79093 0.04731 0.77441 0.05407 0.64706 0.19328\n",
      " 0.84034 0.04202 0.71285 0.07122 0.68895 0.07577 0.66387 0.08403 0.63728\n",
      " 0.08296 0.61345 0.01681 0.58187 0.08757 0.5533  0.08891]  and label  1  is:  0.7045454545454546\n",
      "P Value for  [1.      0.      0.7479  0.0084  0.83312 0.01659 0.82638 0.02469 0.86555\n",
      " 0.01681 0.60504 0.05882 0.79093 0.04731 0.77441 0.05407 0.64706 0.19328\n",
      " 0.84034 0.04202 0.71285 0.07122 0.68895 0.07577 0.66387 0.08403 0.63728\n",
      " 0.08296 0.61345 0.01681 0.58187 0.08757 0.5533  0.08891]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       1.      -0.01081  1.      -0.02703  1.      -0.06486\n",
      "  0.95135 -0.01622  0.98919 -0.03243  0.98919  0.08649  1.      -0.06486\n",
      "  0.95135  0.09189  0.97838 -0.00541  1.       0.06486  1.       0.04324\n",
      "  0.97838  0.09189  0.98556  0.01251  1.      -0.03243  1.       0.02703\n",
      "  1.      -0.07027]  and label  1  is:  0.928030303030303\n",
      "P Value for  [ 1.       0.       1.      -0.01081  1.      -0.02703  1.      -0.06486\n",
      "  0.95135 -0.01622  0.98919 -0.03243  0.98919  0.08649  1.      -0.06486\n",
      "  0.95135  0.09189  0.97838 -0.00541  1.       0.06486  1.       0.04324\n",
      "  0.97838  0.09189  0.98556  0.01251  1.      -0.03243  1.       0.02703\n",
      "  1.      -0.07027]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       1.       0.5782   1.      -1.       1.      -1.\n",
      "  1.      -1.       1.      -1.       1.      -1.       1.      -1.\n",
      "  1.      -1.       1.      -1.       1.      -0.62796  1.      -1.\n",
      "  1.      -1.       1.      -1.       1.      -1.       1.      -1.\n",
      "  1.      -1.     ]  and label  1  is:  0.5492424242424242\n",
      "P Value for  [ 1.       0.       1.       0.5782   1.      -1.       1.      -1.\n",
      "  1.      -1.       1.      -1.       1.      -1.       1.      -1.\n",
      "  1.      -1.       1.      -1.       1.      -0.62796  1.      -1.\n",
      "  1.      -1.       1.      -1.       1.      -1.       1.      -1.\n",
      "  1.      -1.     ]  and label  -1  is:  0.007575757575757576\n",
      " \n",
      "P Value for  [ 1.       0.       0.1859  -0.16667  0.       0.       0.       0.\n",
      "  0.       0.       0.       0.       0.11538 -0.19071  0.       0.\n",
      "  0.       0.       0.       0.       0.       0.      -0.05128 -0.06571\n",
      "  0.07853  0.08974  0.17308 -0.10897  0.125    0.09615  0.02564 -0.04808\n",
      "  0.16827  0.19551]  and label  1  is:  0.03787878787878788\n",
      "P Value for  [ 1.       0.       0.1859  -0.16667  0.       0.       0.       0.\n",
      "  0.       0.       0.       0.       0.11538 -0.19071  0.       0.\n",
      "  0.       0.       0.       0.       0.       0.      -0.05128 -0.06571\n",
      "  0.07853  0.08974  0.17308 -0.10897  0.125    0.09615  0.02564 -0.04808\n",
      "  0.16827  0.19551]  and label  -1  is:  0.36742424242424243\n",
      " \n",
      "P Value for  [ 1.       0.       0.90071  0.01773  1.      -0.01773  0.90071  0.00709\n",
      "  0.84752  0.05674  1.       0.03546  0.97872  0.01064  0.97518  0.03546\n",
      "  1.      -0.03191  0.89716 -0.03191  0.8617   0.07801  1.       0.0922\n",
      "  0.90071  0.0461   0.94305  0.03247  0.94681  0.02482  1.       0.01064\n",
      "  0.93617  0.02128]  and label  1  is:  0.9015151515151515\n",
      "P Value for  [ 1.       0.       0.90071  0.01773  1.      -0.01773  0.90071  0.00709\n",
      "  0.84752  0.05674  1.       0.03546  0.97872  0.01064  0.97518  0.03546\n",
      "  1.      -0.03191  0.89716 -0.03191  0.8617   0.07801  1.       0.0922\n",
      "  0.90071  0.0461   0.94305  0.03247  0.94681  0.02482  1.       0.01064\n",
      "  0.93617  0.02128]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.88853  0.01631  0.92007  0.01305  0.92442  0.01359\n",
      "  0.89179 -0.10223  0.90103 -0.08428  0.9304  -0.01033  0.93094 -0.08918\n",
      "  0.86025 -0.05057  0.89451 -0.04024  0.88418 -0.12126  0.88907 -0.11909\n",
      "  0.8298  -0.14138  0.86453 -0.11808  0.85536 -0.13051  0.83524 -0.12452\n",
      "  0.86786 -0.12235]  and label  1  is:  0.7803030303030303\n",
      "P Value for  [ 1.       0.       0.88853  0.01631  0.92007  0.01305  0.92442  0.01359\n",
      "  0.89179 -0.10223  0.90103 -0.08428  0.9304  -0.01033  0.93094 -0.08918\n",
      "  0.86025 -0.05057  0.89451 -0.04024  0.88418 -0.12126  0.88907 -0.11909\n",
      "  0.8298  -0.14138  0.86453 -0.11808  0.85536 -0.13051  0.83524 -0.12452\n",
      "  0.86786 -0.12235]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 0.  0. -1.  1.  0.  0.  0.  0. -1.  1.  1.  1.  0.  0.  0.  0.  1. -1.\n",
      " -1.  1.  1.  1.  0.  0. -1. -1.  1. -1.  1.  1. -1.  1.  0.  0.]  and label  1  is:  0.05303030303030303\n",
      "P Value for  [ 0.  0. -1.  1.  0.  0.  0.  0. -1.  1.  1.  1.  0.  0.  0.  0.  1. -1.\n",
      " -1.  1.  1.  1.  0.  0. -1. -1.  1. -1.  1.  1. -1.  1.  0.  0.]  and label  -1  is:  0.30303030303030304\n",
      " \n",
      "P Value for  [ 1.       0.       0.51207  1.       1.       0.5381   0.71178  0.80833\n",
      "  0.45622  0.46427  0.33081  1.       0.21249  1.      -0.17416  1.\n",
      " -0.33081  0.98722 -0.61382  1.      -0.52674  0.71699 -0.885    0.47894\n",
      " -1.       0.35175 -1.       0.09569 -1.      -0.16713 -1.      -0.42226\n",
      " -0.91903 -0.65557]  and label  1  is:  0.6628787878787878\n",
      "P Value for  [ 1.       0.       0.51207  1.       1.       0.5381   0.71178  0.80833\n",
      "  0.45622  0.46427  0.33081  1.       0.21249  1.      -0.17416  1.\n",
      " -0.33081  0.98722 -0.61382  1.      -0.52674  0.71699 -0.885    0.47894\n",
      " -1.       0.35175 -1.       0.09569 -1.      -0.16713 -1.      -0.42226\n",
      " -0.91903 -0.65557]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.9408   0.11933  0.85738  0.01038  0.85124  0.01546\n",
      "  0.76966 -0.00278  0.84459  0.10916  0.83289  0.03027  0.8268   0.03506\n",
      "  0.74838  0.01943  0.80019  0.02405  0.80862  0.04901  0.80259  0.05352\n",
      "  0.77336  0.0222   0.79058  0.06235  0.85939  0.09251  0.77863  0.0709\n",
      "  0.77269  0.07508]  and label  1  is:  0.821969696969697\n",
      "P Value for  [ 1.       0.       0.9408   0.11933  0.85738  0.01038  0.85124  0.01546\n",
      "  0.76966 -0.00278  0.84459  0.10916  0.83289  0.03027  0.8268   0.03506\n",
      "  0.74838  0.01943  0.80019  0.02405  0.80862  0.04901  0.80259  0.05352\n",
      "  0.77336  0.0222   0.79058  0.06235  0.85939  0.09251  0.77863  0.0709\n",
      "  0.77269  0.07508]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.0000e+00  0.0000e+00  9.4701e-01 -3.4000e-04  9.3207e-01 -3.2270e-02\n",
      "  9.5177e-01 -3.4310e-02  9.5584e-01  2.4460e-02  9.4124e-01  1.7660e-02\n",
      "  9.2595e-01  4.6880e-02  9.3954e-01 -1.4610e-02  9.4837e-01  2.0040e-02\n",
      "  9.3784e-01  1.3930e-02  9.1406e-01  7.6770e-02  8.9470e-01  6.1480e-02\n",
      "  9.3988e-01  3.1930e-02  9.2489e-01  2.5420e-02  9.2120e-01  2.2420e-02\n",
      "  9.2459e-01  4.4200e-03  9.2697e-01 -5.7700e-03]  and label  1  is:  0.9621212121212122\n",
      "P Value for  [ 1.0000e+00  0.0000e+00  9.4701e-01 -3.4000e-04  9.3207e-01 -3.2270e-02\n",
      "  9.5177e-01 -3.4310e-02  9.5584e-01  2.4460e-02  9.4124e-01  1.7660e-02\n",
      "  9.2595e-01  4.6880e-02  9.3954e-01 -1.4610e-02  9.4837e-01  2.0040e-02\n",
      "  9.3784e-01  1.3930e-02  9.1406e-01  7.6770e-02  8.9470e-01  6.1480e-02\n",
      "  9.3988e-01  3.1930e-02  9.2489e-01  2.5420e-02  9.2120e-01  2.2420e-02\n",
      "  9.2459e-01  4.4200e-03  9.2697e-01 -5.7700e-03]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       1.       0.01105  1.       0.01105  1.       0.0232\n",
      "  0.99448 -0.01436  0.99448 -0.00221  0.98343  0.0232   1.       0.00884\n",
      "  0.97569  0.00773  0.97901  0.01657  0.98011  0.00663  0.98122  0.02099\n",
      "  0.97127 -0.00663  0.98033  0.016    0.97901  0.01547  0.98564  0.02099\n",
      "  0.98674  0.02762]  and label  1  is:  0.9583333333333334\n",
      "P Value for  [ 1.       0.       1.       0.01105  1.       0.01105  1.       0.0232\n",
      "  0.99448 -0.01436  0.99448 -0.00221  0.98343  0.0232   1.       0.00884\n",
      "  0.97569  0.00773  0.97901  0.01657  0.98011  0.00663  0.98122  0.02099\n",
      "  0.97127 -0.00663  0.98033  0.016    0.97901  0.01547  0.98564  0.02099\n",
      "  0.98674  0.02762]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       1.       0.       1.       0.       0.66667  0.11111\n",
      "  1.      -0.11111  0.88889 -0.11111  1.      -0.22222  0.77778  0.\n",
      "  0.77778  0.       1.      -0.11111  0.77778 -0.11111  0.66667 -0.11111\n",
      "  0.66667  0.       0.90347 -0.05352  1.       0.11111  0.88889 -0.11111\n",
      "  1.       0.     ]  and label  1  is:  0.6439393939393939\n",
      "P Value for  [ 1.       0.       1.       0.       1.       0.       0.66667  0.11111\n",
      "  1.      -0.11111  0.88889 -0.11111  1.      -0.22222  0.77778  0.\n",
      "  0.77778  0.       1.      -0.11111  0.77778 -0.11111  0.66667 -0.11111\n",
      "  0.66667  0.       0.90347 -0.05352  1.       0.11111  0.88889 -0.11111\n",
      "  1.       0.     ]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.50112 -0.03596  0.61124  0.01348  0.58876  0.01573\n",
      "  0.58876  0.02472  0.66742 -0.00449  0.71685 -0.04719  0.66517  0.00899\n",
      "  0.57303  0.02472  0.64719 -0.07416  0.56854  0.14157  0.57528 -0.03596\n",
      "  0.46517  0.04944  0.56588  0.00824  0.4764  -0.03596  0.54607  0.10562\n",
      "  0.60674 -0.0809 ]  and label  1  is:  0.6136363636363636\n",
      "P Value for  [ 1.       0.       0.50112 -0.03596  0.61124  0.01348  0.58876  0.01573\n",
      "  0.58876  0.02472  0.66742 -0.00449  0.71685 -0.04719  0.66517  0.00899\n",
      "  0.57303  0.02472  0.64719 -0.07416  0.56854  0.14157  0.57528 -0.03596\n",
      "  0.46517  0.04944  0.56588  0.00824  0.4764  -0.03596  0.54607  0.10562\n",
      "  0.60674 -0.0809 ]  and label  -1  is:  0.007575757575757576\n",
      " \n",
      "P Value for  [ 1.       0.       1.      -0.02259  1.      -0.04494  1.      -0.06682\n",
      "  1.      -0.08799  1.       0.56173  1.      -0.12738  1.      -0.14522\n",
      "  1.       0.32407  1.      -0.17639  0.99484 -0.18949  0.95601 -0.20081\n",
      "  1.      -0.92284  0.8728  -0.21793  0.8292  -0.2237   0.78479 -0.22765\n",
      "  0.73992 -0.22981]  and label  1  is:  0.5113636363636364\n",
      "P Value for  [ 1.       0.       1.      -0.02259  1.      -0.04494  1.      -0.06682\n",
      "  1.      -0.08799  1.       0.56173  1.      -0.12738  1.      -0.14522\n",
      "  1.       0.32407  1.      -0.17639  0.99484 -0.18949  0.95601 -0.20081\n",
      "  1.      -0.92284  0.8728  -0.21793  0.8292  -0.2237   0.78479 -0.22765\n",
      "  0.73992 -0.22981]  and label  -1  is:  0.007575757575757576\n",
      " \n",
      "P Value for  [ 1.       0.       1.      -0.06604  1.       0.62937  1.       0.09557\n",
      "  1.       0.2028   1.      -1.       1.      -0.40559  1.      -0.15851\n",
      "  1.       0.04895  1.      -0.61538  1.      -0.26573  1.      -1.\n",
      "  1.      -0.58042  1.      -0.81372  1.      -1.       1.      -0.78555\n",
      "  1.      -0.48252]  and label  1  is:  0.5227272727272727\n",
      "P Value for  [ 1.       0.       1.      -0.06604  1.       0.62937  1.       0.09557\n",
      "  1.       0.2028   1.      -1.       1.      -0.40559  1.      -0.15851\n",
      "  1.       0.04895  1.      -0.61538  1.      -0.26573  1.      -1.\n",
      "  1.      -0.58042  1.      -0.81372  1.      -1.       1.      -0.78555\n",
      "  1.      -0.48252]  and label  -1  is:  0.007575757575757576\n",
      " \n",
      "P Value for  [ 1.       0.       0.86889 -0.07111  1.      -0.02494  1.      -0.06889\n",
      "  0.87778  0.00222  0.83556 -0.06444  1.      -0.07287  1.      -0.2\n",
      "  0.86889  0.05333  0.88    -0.03778  1.      -0.11526  1.      -0.18667\n",
      "  0.84444  0.03556  1.      -0.14162  0.82222 -0.14667  1.      -0.15609\n",
      "  1.      -0.44222]  and label  1  is:  0.7272727272727273\n",
      "P Value for  [ 1.       0.       0.86889 -0.07111  1.      -0.02494  1.      -0.06889\n",
      "  0.87778  0.00222  0.83556 -0.06444  1.      -0.07287  1.      -0.2\n",
      "  0.86889  0.05333  0.88    -0.03778  1.      -0.11526  1.      -0.18667\n",
      "  0.84444  0.03556  1.      -0.14162  0.82222 -0.14667  1.      -0.15609\n",
      "  1.      -0.44222]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 0.       0.       1.      -1.       1.      -1.       1.      -1.\n",
      "  1.      -1.       1.       1.       1.       1.       1.      -1.\n",
      "  1.       1.       1.       1.       1.       1.       1.      -1.\n",
      "  1.      -1.       1.      -1.       1.       0.65625  0.       0.\n",
      "  1.      -1.     ]  and label  1  is:  0.08712121212121213\n",
      "P Value for  [ 0.       0.       1.      -1.       1.      -1.       1.      -1.\n",
      "  1.      -1.       1.       1.       1.       1.       1.      -1.\n",
      "  1.       1.       1.       1.       1.       1.       1.      -1.\n",
      "  1.      -1.       1.      -1.       1.       0.65625  0.       0.\n",
      "  1.      -1.     ]  and label  -1  is:  0.1893939393939394\n",
      " \n",
      "P Value for  [ 1.       0.       0.99025 -0.05785  0.99793 -0.13009  0.98663 -0.1943\n",
      "  0.99374 -0.25843  0.92738 -0.3013   0.92651 -0.37965  0.89812 -0.43796\n",
      "  0.84922 -0.52064  0.87433 -0.57075  0.79016 -0.59839  0.74725 -0.64615\n",
      "  0.68282 -0.68479  0.65247 -0.73174  0.6101  -0.75353  0.54752 -0.80278\n",
      "  0.49195 -0.83245]  and label  1  is:  0.7234848484848485\n",
      "P Value for  [ 1.       0.       0.99025 -0.05785  0.99793 -0.13009  0.98663 -0.1943\n",
      "  0.99374 -0.25843  0.92738 -0.3013   0.92651 -0.37965  0.89812 -0.43796\n",
      "  0.84922 -0.52064  0.87433 -0.57075  0.79016 -0.59839  0.74725 -0.64615\n",
      "  0.68282 -0.68479  0.65247 -0.73174  0.6101  -0.75353  0.54752 -0.80278\n",
      "  0.49195 -0.83245]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       1.      -0.00612  1.      -0.09834  1.      -0.07649\n",
      "  1.      -0.10605  1.      -0.11073  1.      -0.39489  1.      -0.15616\n",
      "  0.92124 -0.31884  0.86473 -0.34534  0.91693 -0.44072  0.9606  -0.46866\n",
      "  0.81874 -0.40372  0.82681 -0.42231  0.75784 -0.38231  0.80448 -0.40575\n",
      "  0.74354 -0.45039]  and label  1  is:  0.7121212121212122\n",
      "P Value for  [ 1.       0.       1.      -0.00612  1.      -0.09834  1.      -0.07649\n",
      "  1.      -0.10605  1.      -0.11073  1.      -0.39489  1.      -0.15616\n",
      "  0.92124 -0.31884  0.86473 -0.34534  0.91693 -0.44072  0.9606  -0.46866\n",
      "  0.81874 -0.40372  0.82681 -0.42231  0.75784 -0.38231  0.80448 -0.40575\n",
      "  0.74354 -0.45039]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       1.       0.28046  1.       0.02477  1.       0.07764\n",
      "  1.       0.04317  0.98762  0.33266  1.       0.05489  1.       0.04384\n",
      "  0.9575  -0.24598  0.84371 -0.08668  1.       0.0415   0.99933  0.27376\n",
      "  1.      -0.39056  0.96414 -0.02174  0.86747  0.2336   0.94578 -0.22021\n",
      "  0.80355 -0.07329]  and label  1  is:  0.6590909090909091\n",
      "P Value for  [ 1.       0.       1.       0.28046  1.       0.02477  1.       0.07764\n",
      "  1.       0.04317  0.98762  0.33266  1.       0.05489  1.       0.04384\n",
      "  0.9575  -0.24598  0.84371 -0.08668  1.       0.0415   0.99933  0.27376\n",
      "  1.      -0.39056  0.96414 -0.02174  0.86747  0.2336   0.94578 -0.22021\n",
      "  0.80355 -0.07329]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.65845  0.43617  0.44681  0.74804  0.05319  0.85106\n",
      " -0.32027  0.82139 -0.68253  0.52408 -0.84211  0.07111 -0.82811 -0.28723\n",
      " -0.47032 -0.71725 -0.04759 -0.86002  0.23292 -0.76316  0.56663 -0.52128\n",
      "  0.743   -0.18645  0.74758  0.23713  0.45185  0.59071  0.20549  0.76764\n",
      " -0.18533  0.74356]  and label  1  is:  0.8409090909090909\n",
      "P Value for  [ 1.       0.       0.65845  0.43617  0.44681  0.74804  0.05319  0.85106\n",
      " -0.32027  0.82139 -0.68253  0.52408 -0.84211  0.07111 -0.82811 -0.28723\n",
      " -0.47032 -0.71725 -0.04759 -0.86002  0.23292 -0.76316  0.56663 -0.52128\n",
      "  0.743   -0.18645  0.74758  0.23713  0.45185  0.59071  0.20549  0.76764\n",
      " -0.18533  0.74356]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.93537  0.13645  0.93716  0.25359  0.85705  0.38779\n",
      "  0.79039  0.47127  0.72352  0.59942  0.6526   0.75     0.5083   0.73586\n",
      "  0.41629  0.82742  0.25539  0.85952  0.13712  0.85615  0.00494  0.88869\n",
      " -0.07361  0.7978  -0.20995  0.78004 -0.33169  0.71454 -0.38532  0.64363\n",
      " -0.47419  0.55835]  and label  1  is:  0.8939393939393939\n",
      "P Value for  [ 1.       0.       0.93537  0.13645  0.93716  0.25359  0.85705  0.38779\n",
      "  0.79039  0.47127  0.72352  0.59942  0.6526   0.75     0.5083   0.73586\n",
      "  0.41629  0.82742  0.25539  0.85952  0.13712  0.85615  0.00494  0.88869\n",
      " -0.07361  0.7978  -0.20995  0.78004 -0.33169  0.71454 -0.38532  0.64363\n",
      " -0.47419  0.55835]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       1.       0.05812  0.94525  0.07418  0.99952  0.13231\n",
      "  1.      -0.01911  0.94846  0.07033  0.95713  0.14644  0.94862  0.11224\n",
      "  0.90896  0.20119  0.96741  0.16265  0.99695  0.14258  0.90784  0.1641\n",
      "  0.91667  0.22431  0.88423  0.23571  0.88568  0.22511  0.78324  0.29576\n",
      "  0.83574  0.31166]  and label  1  is:  0.7196969696969697\n",
      "P Value for  [ 1.       0.       1.       0.05812  0.94525  0.07418  0.99952  0.13231\n",
      "  1.      -0.01911  0.94846  0.07033  0.95713  0.14644  0.94862  0.11224\n",
      "  0.90896  0.20119  0.96741  0.16265  0.99695  0.14258  0.90784  0.1641\n",
      "  0.91667  0.22431  0.88423  0.23571  0.88568  0.22511  0.78324  0.29576\n",
      "  0.83574  0.31166]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.76627  0.21106  0.63935  0.38112  0.48409  0.525\n",
      "  0.15     0.22273  0.13753  0.59565 -0.07727  0.44545  0.       0.48636\n",
      " -0.27491  0.42014 -0.56136  0.36818 -0.36591  0.18864 -0.40533  0.07588\n",
      " -0.38483 -0.03229 -0.33942 -0.12486 -0.2754  -0.19714 -0.19962 -0.24648\n",
      " -0.11894 -0.27218]  and label  1  is:  0.3939393939393939\n",
      "P Value for  [ 1.       0.       0.76627  0.21106  0.63935  0.38112  0.48409  0.525\n",
      "  0.15     0.22273  0.13753  0.59565 -0.07727  0.44545  0.       0.48636\n",
      " -0.27491  0.42014 -0.56136  0.36818 -0.36591  0.18864 -0.40533  0.07588\n",
      " -0.38483 -0.03229 -0.33942 -0.12486 -0.2754  -0.19714 -0.19962 -0.24648\n",
      " -0.11894 -0.27218]  and label  -1  is:  0.030303030303030304\n",
      " \n",
      "P Value for  [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]  and label  1  is:  0.007575757575757576\n",
      "P Value for  [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]  and label  -1  is:  0.5568181818181818\n",
      " \n",
      "P Value for  [ 1.       0.       0.85209  0.39252  0.38887  0.76432  0.08858  0.98903\n",
      " -0.42625  0.88744 -0.76229  0.4998  -0.93092  0.10768 -0.859   -0.31044\n",
      " -0.6603  -0.55262 -0.1926  -0.86063  0.28444 -0.80496  0.64649 -0.3523\n",
      "  0.77814 -0.23324  0.71698  0.21343  0.3783   0.5831   0.19667  0.66315\n",
      " -0.11215  0.64933]  and label  1  is:  0.8522727272727273\n",
      "P Value for  [ 1.       0.       0.85209  0.39252  0.38887  0.76432  0.08858  0.98903\n",
      " -0.42625  0.88744 -0.76229  0.4998  -0.93092  0.10768 -0.859   -0.31044\n",
      " -0.6603  -0.55262 -0.1926  -0.86063  0.28444 -0.80496  0.64649 -0.3523\n",
      "  0.77814 -0.23324  0.71698  0.21343  0.3783   0.5831   0.19667  0.66315\n",
      " -0.11215  0.64933]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       1.       0.67784  0.81309  0.82021  0.43019  1.\n",
      "  0.20619  0.80541 -0.43872  1.      -0.79135  0.77092 -1.       0.40268\n",
      " -0.39046 -0.58634 -0.97907 -0.42822 -0.73083 -0.76339 -0.37671 -0.97491\n",
      "  0.41366 -1.       0.41778 -0.93296  0.25773 -1.       0.9357  -0.35222\n",
      "  0.98816  0.03446]  and label  1  is:  0.696969696969697\n",
      "P Value for  [ 1.       0.       1.       0.67784  0.81309  0.82021  0.43019  1.\n",
      "  0.20619  0.80541 -0.43872  1.      -0.79135  0.77092 -1.       0.40268\n",
      " -0.39046 -0.58634 -0.97907 -0.42822 -0.73083 -0.76339 -0.37671 -0.97491\n",
      "  0.41366 -1.       0.41778 -0.93296  0.25773 -1.       0.9357  -0.35222\n",
      "  0.98816  0.03446]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.74852 -0.02811  0.6568  -0.05178  0.80621  0.02811\n",
      "  0.85947  0.02515  0.63462  0.08728  0.71598  0.0784   0.73077  0.05178\n",
      "  0.7855  -0.27811  0.65976 -0.01479  0.78698  0.06953  0.34615 -0.18639\n",
      "  0.65385  0.02811  0.61009 -0.06637  0.5355  -0.21154  0.59024 -0.14053\n",
      "  0.56361  0.02959]  and label  1  is:  0.4166666666666667\n",
      "P Value for  [ 1.       0.       0.74852 -0.02811  0.6568  -0.05178  0.80621  0.02811\n",
      "  0.85947  0.02515  0.63462  0.08728  0.71598  0.0784   0.73077  0.05178\n",
      "  0.7855  -0.27811  0.65976 -0.01479  0.78698  0.06953  0.34615 -0.18639\n",
      "  0.65385  0.02811  0.61009 -0.06637  0.5355  -0.21154  0.59024 -0.14053\n",
      "  0.56361  0.02959]  and label  -1  is:  0.011363636363636364\n",
      " \n",
      "P Value for  [ 1.       0.      -1.      -0.59677  0.       0.      -1.       0.64516\n",
      " -0.87097  1.       0.       0.       0.       0.       0.       0.\n",
      "  0.       0.       0.       0.      -1.      -1.       0.       0.\n",
      "  0.29839  0.23387  1.       0.51613  0.       0.       0.       0.\n",
      "  0.       0.     ]  and label  1  is:  0.17424242424242425\n",
      "P Value for  [ 1.       0.      -1.      -0.59677  0.       0.      -1.       0.64516\n",
      " -0.87097  1.       0.       0.       0.       0.       0.       0.\n",
      "  0.       0.       0.       0.      -1.      -1.       0.       0.\n",
      "  0.29839  0.23387  1.       0.51613  0.       0.       0.       0.\n",
      "  0.       0.     ]  and label  -1  is:  0.0946969696969697\n",
      " \n",
      "P Value for  [ 1.0000e+00  0.0000e+00  2.5000e-01  1.6667e-01  4.6667e-01  2.6667e-01\n",
      "  1.9036e-01  2.3966e-01  7.7660e-02  1.9939e-01  1.0700e-02  1.4922e-01\n",
      " -2.3670e-02  1.0188e-01 -3.6850e-02  6.3170e-02 -3.7660e-02  3.4580e-02\n",
      " -3.2300e-02  1.5320e-02 -2.4740e-02  3.5700e-03 -1.7260e-02 -2.7300e-03\n",
      " -1.0970e-02 -5.3900e-03 -6.2100e-03 -5.8600e-03 -2.9400e-03 -5.2000e-03\n",
      " -8.9000e-04 -4.0800e-03  2.5000e-04 -2.9100e-03]  and label  1  is:  0.2803030303030303\n",
      "P Value for  [ 1.0000e+00  0.0000e+00  2.5000e-01  1.6667e-01  4.6667e-01  2.6667e-01\n",
      "  1.9036e-01  2.3966e-01  7.7660e-02  1.9939e-01  1.0700e-02  1.4922e-01\n",
      " -2.3670e-02  1.0188e-01 -3.6850e-02  6.3170e-02 -3.7660e-02  3.4580e-02\n",
      " -3.2300e-02  1.5320e-02 -2.4740e-02  3.5700e-03 -1.7260e-02 -2.7300e-03\n",
      " -1.0970e-02 -5.3900e-03 -6.2100e-03 -5.8600e-03 -2.9400e-03 -5.2000e-03\n",
      " -8.9000e-04 -4.0800e-03  2.5000e-04 -2.9100e-03]  and label  -1  is:  0.06060606060606061\n",
      " \n",
      "P Value for  [ 1.       0.       1.       0.5      1.       0.25     0.25     1.\n",
      "  0.16851  0.9118  -0.13336  0.80454 -0.34107  0.60793 -0.4382   0.37856\n",
      " -0.43663  0.16709 -0.36676  0.00678 -0.26477 -0.09025 -0.16178 -0.12964\n",
      " -0.07782 -0.12744 -0.02089 -0.10242  0.01033 -0.07036  0.02224 -0.04142\n",
      "  0.02249 -0.02017]  and label  1  is:  0.4962121212121212\n",
      "P Value for  [ 1.       0.       1.       0.5      1.       0.25     0.25     1.\n",
      "  0.16851  0.9118  -0.13336  0.80454 -0.34107  0.60793 -0.4382   0.37856\n",
      " -0.43663  0.16709 -0.36676  0.00678 -0.26477 -0.09025 -0.16178 -0.12964\n",
      " -0.07782 -0.12744 -0.02089 -0.10242  0.01033 -0.07036  0.02224 -0.04142\n",
      "  0.02249 -0.02017]  and label  -1  is:  0.007575757575757576\n",
      " \n",
      "P Value for  [ 1.       0.       0.88179  0.43491  0.59573  0.77655  0.19672  0.94537\n",
      " -0.24103  0.92544 -0.62526  0.71257 -0.86443  0.33652 -0.92384 -0.05338\n",
      " -0.77356 -0.44707 -0.4695  -0.73285 -0.10237 -0.82217  0.26384 -0.7757\n",
      "  0.55984 -0.5591   0.72147 -0.24433  0.72478  0.09599  0.58137  0.38915\n",
      "  0.34749  0.57656]  and label  1  is:  0.7537878787878788\n",
      "P Value for  [ 1.       0.       0.88179  0.43491  0.59573  0.77655  0.19672  0.94537\n",
      " -0.24103  0.92544 -0.62526  0.71257 -0.86443  0.33652 -0.92384 -0.05338\n",
      " -0.77356 -0.44707 -0.4695  -0.73285 -0.10237 -0.82217  0.26384 -0.7757\n",
      "  0.55984 -0.5591   0.72147 -0.24433  0.72478  0.09599  0.58137  0.38915\n",
      "  0.34749  0.57656]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.88085  0.35232  0.68389  0.65128  0.34816  0.79784\n",
      "  0.05832  0.90842 -0.29784  0.8649  -0.62635  0.6959  -0.77106  0.39309\n",
      " -0.85803  0.08408 -0.81641 -0.24017 -0.64579 -0.50022 -0.39766 -0.68337\n",
      " -0.11147 -0.75533  0.17041 -0.71504  0.40675 -0.57649  0.56626 -0.36765\n",
      "  0.62765 -0.13305]  and label  1  is:  0.8977272727272727\n",
      "P Value for  [ 1.       0.       0.88085  0.35232  0.68389  0.65128  0.34816  0.79784\n",
      "  0.05832  0.90842 -0.29784  0.8649  -0.62635  0.6959  -0.77106  0.39309\n",
      " -0.85803  0.08408 -0.81641 -0.24017 -0.64579 -0.50022 -0.39766 -0.68337\n",
      " -0.11147 -0.75533  0.17041 -0.71504  0.40675 -0.57649  0.56626 -0.36765\n",
      "  0.62765 -0.13305]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.0000e+00  0.0000e+00  9.9449e-01  5.2600e-03  8.4082e-01 -1.1313e-01\n",
      "  8.8237e-01 -1.6431e-01  9.9061e-01 -6.2570e-02  9.6484e-01 -7.4960e-02\n",
      "  8.5221e-01  2.9660e-02  8.7161e-01 -2.0848e-01  9.3881e-01 -1.2977e-01\n",
      "  9.8298e-01 -8.9350e-02  8.9876e-01  7.5000e-04  8.7836e-01 -5.8820e-02\n",
      "  9.3368e-01 -1.9872e-01  8.7579e-01 -1.7806e-01  9.4294e-01 -1.6581e-01\n",
      "  8.0253e-01 -2.5741e-01  7.6586e-01 -2.7794e-01]  and label  1  is:  0.7234848484848485\n",
      "P Value for  [ 1.0000e+00  0.0000e+00  9.9449e-01  5.2600e-03  8.4082e-01 -1.1313e-01\n",
      "  8.8237e-01 -1.6431e-01  9.9061e-01 -6.2570e-02  9.6484e-01 -7.4960e-02\n",
      "  8.5221e-01  2.9660e-02  8.7161e-01 -2.0848e-01  9.3881e-01 -1.2977e-01\n",
      "  9.8298e-01 -8.9350e-02  8.9876e-01  7.5000e-04  8.7836e-01 -5.8820e-02\n",
      "  9.3368e-01 -1.9872e-01  8.7579e-01 -1.7806e-01  9.4294e-01 -1.6581e-01\n",
      "  8.0253e-01 -2.5741e-01  7.6586e-01 -2.7794e-01]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.74449 -0.0239   0.70772  0.03309  0.72243  0.16912\n",
      "  0.79228  0.07721  0.81434  0.43934  0.63787  0.00551  0.70772  0.21691\n",
      "  1.       0.06066  0.61029  0.05147  0.67463  0.04228  0.52022 -0.25\n",
      "  0.72978 -0.15809  0.61727  0.07124  0.30882  0.0864   0.55916  0.07458\n",
      "  0.60294  0.21691]  and label  1  is:  0.49242424242424243\n",
      "P Value for  [ 1.       0.       0.74449 -0.0239   0.70772  0.03309  0.72243  0.16912\n",
      "  0.79228  0.07721  0.81434  0.43934  0.63787  0.00551  0.70772  0.21691\n",
      "  1.       0.06066  0.61029  0.05147  0.67463  0.04228  0.52022 -0.25\n",
      "  0.72978 -0.15809  0.61727  0.07124  0.30882  0.0864   0.55916  0.07458\n",
      "  0.60294  0.21691]  and label  -1  is:  0.007575757575757576\n",
      " \n",
      "P Value for  [ 1.       0.       1.      -1.       0.       0.       0.77941 -0.99265\n",
      "  0.80882  0.55147 -0.41912 -0.94853  0.       0.       0.       0.\n",
      "  0.72059 -0.77206  0.73529 -0.60294  0.       0.       0.18382 -1.\n",
      " -1.      -1.      -1.      -1.       1.      -1.       1.      -1.\n",
      "  0.       0.     ]  and label  1  is:  0.3143939393939394\n",
      "P Value for  [ 1.       0.       1.      -1.       0.       0.       0.77941 -0.99265\n",
      "  0.80882  0.55147 -0.41912 -0.94853  0.       0.       0.       0.\n",
      "  0.72059 -0.77206  0.73529 -0.60294  0.       0.       0.18382 -1.\n",
      " -1.      -1.      -1.      -1.       1.      -1.       1.      -1.\n",
      "  0.       0.     ]  and label  -1  is:  0.04924242424242424\n",
      " \n",
      "P Value for  [ 1.       0.       1.       0.23058  1.      -0.78509  1.      -0.10401\n",
      "  1.       0.15414  1.       0.2782   0.9812  -0.06861  1.       0.0661\n",
      "  0.95802 -0.18954  0.83584 -0.15633  0.974    0.03728  0.99624  0.09242\n",
      "  1.      -0.01253  0.96238 -0.04597  0.91165  0.03885  1.      -0.13722\n",
      "  0.96523 -0.11717]  and label  1  is:  0.5833333333333334\n",
      "P Value for  [ 1.       0.       1.       0.23058  1.      -0.78509  1.      -0.10401\n",
      "  1.       0.15414  1.       0.2782   0.9812  -0.06861  1.       0.0661\n",
      "  0.95802 -0.18954  0.83584 -0.15633  0.974    0.03728  0.99624  0.09242\n",
      "  1.      -0.01253  0.96238 -0.04597  0.91165  0.03885  1.      -0.13722\n",
      "  0.96523 -0.11717]  and label  -1  is:  0.007575757575757576\n",
      " \n",
      "P Value for  [ 1.       0.       1.       0.24077  0.99815  0.00369  0.80244 -0.30133\n",
      "  0.89919 -0.23486  0.70643 -0.24077  0.73855 -0.30539  0.71492 -0.36078\n",
      "  0.47194 -0.61189  0.40473 -0.55059  0.61041 -0.39328  0.53176 -0.32681\n",
      "  0.23966 -0.52142  0.29208 -0.4839   0.12777 -0.39143  0.15657 -0.51329\n",
      "  0.18353 -0.46603]  and label  1  is:  0.45075757575757575\n",
      "P Value for  [ 1.       0.       1.       0.24077  0.99815  0.00369  0.80244 -0.30133\n",
      "  0.89919 -0.23486  0.70643 -0.24077  0.73855 -0.30539  0.71492 -0.36078\n",
      "  0.47194 -0.61189  0.40473 -0.55059  0.61041 -0.39328  0.53176 -0.32681\n",
      "  0.23966 -0.52142  0.29208 -0.4839   0.12777 -0.39143  0.15657 -0.51329\n",
      "  0.18353 -0.46603]  and label  -1  is:  0.007575757575757576\n",
      " \n",
      "P Value for  [ 1.       0.       0.8941   0.13425  0.87001  0.31543  0.78896  0.43388\n",
      "  0.63388  0.59975  0.54003  0.71016  0.39699  0.76161  0.24266  0.79523\n",
      "  0.09134  0.79598 -0.09159  0.76261 -0.20201  0.66926 -0.30263  0.6261\n",
      " -0.40552  0.50489 -0.46215  0.40753 -0.50314  0.27252 -0.52823  0.19172\n",
      " -0.48808  0.05972]  and label  1  is:  0.7878787878787878\n",
      "P Value for  [ 1.       0.       0.8941   0.13425  0.87001  0.31543  0.78896  0.43388\n",
      "  0.63388  0.59975  0.54003  0.71016  0.39699  0.76161  0.24266  0.79523\n",
      "  0.09134  0.79598 -0.09159  0.76261 -0.20201  0.66926 -0.30263  0.6261\n",
      " -0.40552  0.50489 -0.46215  0.40753 -0.50314  0.27252 -0.52823  0.19172\n",
      " -0.48808  0.05972]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       1.       0.01709  0.96215 -0.03142  1.      -0.03436\n",
      "  1.      -0.05071  0.99026 -0.07092  0.99173 -0.09002  1.      -0.15727\n",
      "  1.      -0.14257  0.9831  -0.11813  1.      -0.18519  1.      -0.19272\n",
      "  0.98971 -0.22083  0.9649  -0.20243  0.94599 -0.17123  0.96436 -0.22561\n",
      "  0.87011 -0.23296]  and label  1  is:  0.8863636363636364\n",
      "P Value for  [ 1.       0.       1.       0.01709  0.96215 -0.03142  1.      -0.03436\n",
      "  1.      -0.05071  0.99026 -0.07092  0.99173 -0.09002  1.      -0.15727\n",
      "  1.      -0.14257  0.9831  -0.11813  1.      -0.18519  1.      -0.19272\n",
      "  0.98971 -0.22083  0.9649  -0.20243  0.94599 -0.17123  0.96436 -0.22561\n",
      "  0.87011 -0.23296]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.01975  0.00705  0.0409  -0.00846  0.02116  0.01128\n",
      "  0.01128  0.04372  0.00282  0.00141  0.01975 -0.03103 -0.01975  0.06065\n",
      " -0.0409   0.0268  -0.02398 -0.00423  0.04372 -0.02539  0.01834  0.\n",
      "  0.      -0.01269  0.01834 -0.01128  0.00564 -0.01551 -0.01693 -0.02398\n",
      "  0.00705  0.     ]  and label  1  is:  0.003787878787878788\n",
      "P Value for  [ 1.       0.       0.01975  0.00705  0.0409  -0.00846  0.02116  0.01128\n",
      "  0.01128  0.04372  0.00282  0.00141  0.01975 -0.03103 -0.01975  0.06065\n",
      " -0.0409   0.0268  -0.02398 -0.00423  0.04372 -0.02539  0.01834  0.\n",
      "  0.      -0.01269  0.01834 -0.01128  0.00564 -0.01551 -0.01693 -0.02398\n",
      "  0.00705  0.     ]  and label  -1  is:  0.6515151515151515\n",
      " \n",
      "P Value for  [ 1.       0.       0.32789  0.11042  0.1597   0.29308  0.1402   0.74485\n",
      " -0.25131  0.91993 -0.16503  0.26664 -0.63714  0.24865 -0.9765  -0.00337\n",
      " -0.23227 -0.19909 -0.30522 -0.48886 -0.14426 -0.89991  0.09345 -0.28916\n",
      "  0.28307 -0.1856   0.39599 -0.11498  0.31005  0.05614  0.21443  0.2054\n",
      "  0.13376  0.26422]  and label  1  is:  0.8939393939393939\n",
      "P Value for  [ 1.       0.       0.32789  0.11042  0.1597   0.29308  0.1402   0.74485\n",
      " -0.25131  0.91993 -0.16503  0.26664 -0.63714  0.24865 -0.9765  -0.00337\n",
      " -0.23227 -0.19909 -0.30522 -0.48886 -0.14426 -0.89991  0.09345 -0.28916\n",
      "  0.28307 -0.1856   0.39599 -0.11498  0.31005  0.05614  0.21443  0.2054\n",
      "  0.13376  0.26422]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.67547  0.04528  0.76981 -0.10566  0.77358  0.03774\n",
      "  0.66038 -0.04528  0.64528  0.01132  0.66792 -0.13962  0.72075 -0.02264\n",
      "  0.76981  0.08679  0.61887 -0.07925  0.75849 -0.23774  0.73962 -0.14717\n",
      "  0.84906 -0.15094  0.73886 -0.05801  0.66792  0.02264  0.86415  0.03774\n",
      "  0.73208  0.00755]  and label  1  is:  0.5227272727272727\n",
      "P Value for  [ 1.       0.       0.67547  0.04528  0.76981 -0.10566  0.77358  0.03774\n",
      "  0.66038 -0.04528  0.64528  0.01132  0.66792 -0.13962  0.72075 -0.02264\n",
      "  0.76981  0.08679  0.61887 -0.07925  0.75849 -0.23774  0.73962 -0.14717\n",
      "  0.84906 -0.15094  0.73886 -0.05801  0.66792  0.02264  0.86415  0.03774\n",
      "  0.73208  0.00755]  and label  -1  is:  0.007575757575757576\n",
      " \n",
      "P Value for  [ 1.       0.       0.6351  -0.04388  0.7653   0.02968  0.61432  0.36028\n",
      "  0.65358 -0.00462  0.64203  0.08314  0.79446 -0.43418  0.72517  0.54965\n",
      "  0.59584  0.13857  0.6351   0.2194   0.63279 -0.25404  0.70951  0.15359\n",
      "  0.64665  0.23095  0.68775  0.17704  0.61663  0.07621  0.66316  0.19841\n",
      "  0.69053  0.36721]  and label  1  is:  0.4696969696969697\n",
      "P Value for  [ 1.       0.       0.6351  -0.04388  0.7653   0.02968  0.61432  0.36028\n",
      "  0.65358 -0.00462  0.64203  0.08314  0.79446 -0.43418  0.72517  0.54965\n",
      "  0.59584  0.13857  0.6351   0.2194   0.63279 -0.25404  0.70951  0.15359\n",
      "  0.64665  0.23095  0.68775  0.17704  0.61663  0.07621  0.66316  0.19841\n",
      "  0.69053  0.36721]  and label  -1  is:  0.007575757575757576\n",
      " \n",
      "P Value for  [ 0.  0.  1. -1.  1.  1.  1. -1.  1.  1.  1. -1.  1. -1.  1. -1.  1.  1.\n",
      "  1.  1.  1.  1.  1. -1.  1.  1.  1.  1.  1.  1.  1.  1.  1. -1.]  and label  1  is:  0.12121212121212122\n",
      "P Value for  [ 0.  0.  1. -1.  1.  1.  1. -1.  1.  1.  1. -1.  1. -1.  1. -1.  1.  1.\n",
      "  1.  1.  1.  1.  1. -1.  1.  1.  1.  1.  1.  1.  1.  1.  1. -1.]  and label  -1  is:  0.14015151515151514\n",
      " \n",
      "P Value for  [ 1.       0.       1.      -0.18829  0.93035 -0.36156 -0.10868 -0.93597\n",
      "  1.      -0.04549  0.50874 -0.67743  0.34432 -0.69707 -0.51685 -0.97515\n",
      "  0.05499 -0.62237  0.33109 -1.      -0.13151 -0.453   -0.18056 -0.35734\n",
      " -0.20332 -0.26569 -0.20468 -0.18401 -0.1904  -0.11593 -0.16626 -0.06288\n",
      " -0.13738 -0.02447]  and label  1  is:  0.30303030303030304\n",
      "P Value for  [ 1.       0.       1.      -0.18829  0.93035 -0.36156 -0.10868 -0.93597\n",
      "  1.      -0.04549  0.50874 -0.67743  0.34432 -0.69707 -0.51685 -0.97515\n",
      "  0.05499 -0.62237  0.33109 -1.      -0.13151 -0.453   -0.18056 -0.35734\n",
      " -0.20332 -0.26569 -0.20468 -0.18401 -0.1904  -0.11593 -0.16626 -0.06288\n",
      " -0.13738 -0.02447]  and label  -1  is:  0.05303030303030303\n",
      " \n",
      "P Value for  [ 1.       0.       0.62121 -0.63636  0.       0.       0.       0.\n",
      "  0.3447   0.28788  0.42803  0.39394 -0.07576  0.51894  0.36364  0.31439\n",
      " -0.53788  0.32955  0.12121 -0.14773  0.01894 -0.53409 -0.57576  0.17803\n",
      "  0.29167 -0.27273  0.25758 -0.57576  0.43182  0.24242  0.18182 -0.02273\n",
      "  0.17045 -0.41667]  and label  1  is:  0.16666666666666666\n",
      "P Value for  [ 1.       0.       0.62121 -0.63636  0.       0.       0.       0.\n",
      "  0.3447   0.28788  0.42803  0.39394 -0.07576  0.51894  0.36364  0.31439\n",
      " -0.53788  0.32955  0.12121 -0.14773  0.01894 -0.53409 -0.57576  0.17803\n",
      "  0.29167 -0.27273  0.25758 -0.57576  0.43182  0.24242  0.18182 -0.02273\n",
      "  0.17045 -0.41667]  and label  -1  is:  0.10227272727272728\n",
      " \n",
      "P Value for  [ 1.       0.       1.      -0.0373   1.      -0.07383  0.99601 -0.11039\n",
      "  0.99838 -0.09931  0.98941 -0.13814  0.96674 -0.21695  0.95288 -0.25099\n",
      "  0.91236 -0.344    0.90581 -0.32152  0.89991 -0.34691  0.87874 -0.37643\n",
      "  0.86213 -0.4299   0.83172 -0.43122  0.81433 -0.42593  0.77919 -0.47977\n",
      "  0.75115 -0.50152]  and label  1  is:  0.7537878787878788\n",
      "P Value for  [ 1.       0.       1.      -0.0373   1.      -0.07383  0.99601 -0.11039\n",
      "  0.99838 -0.09931  0.98941 -0.13814  0.96674 -0.21695  0.95288 -0.25099\n",
      "  0.91236 -0.344    0.90581 -0.32152  0.89991 -0.34691  0.87874 -0.37643\n",
      "  0.86213 -0.4299   0.83172 -0.43122  0.81433 -0.42593  0.77919 -0.47977\n",
      "  0.75115 -0.50152]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.08696  0.00686  0.13959 -0.04119  0.10526 -0.08238\n",
      "  0.12586 -0.06178  0.23341 -0.01144  0.12357  0.0778   0.14645 -0.13501\n",
      "  0.29062 -0.04805  0.18993  0.07323  0.1167   0.       0.11213 -0.00229\n",
      "  0.15103 -0.10297  0.08467  0.01373  0.11213 -0.06636  0.09611 -0.07323\n",
      "  0.1167  -0.06865]  and label  1  is:  0.06818181818181818\n",
      "P Value for  [ 1.       0.       0.08696  0.00686  0.13959 -0.04119  0.10526 -0.08238\n",
      "  0.12586 -0.06178  0.23341 -0.01144  0.12357  0.0778   0.14645 -0.13501\n",
      "  0.29062 -0.04805  0.18993  0.07323  0.1167   0.       0.11213 -0.00229\n",
      "  0.15103 -0.10297  0.08467  0.01373  0.11213 -0.06636  0.09611 -0.07323\n",
      "  0.1167  -0.06865]  and label  -1  is:  0.25757575757575757\n",
      " \n",
      "P Value for  [ 1.       0.       0.66667 -0.01366  0.97404  0.06831  0.4959   0.50137\n",
      "  0.75683 -0.00273  0.65164 -0.14071  0.40164 -0.48907  0.39208  0.58743\n",
      "  0.76776  0.31831  0.78552  0.11339  0.47541 -0.44945  1.       0.00683\n",
      "  0.60656  0.06967  0.68656  0.17088  0.87568  0.07787  0.55328  0.2459\n",
      "  0.13934  0.48087]  and label  1  is:  0.3333333333333333\n",
      "P Value for  [ 1.       0.       0.66667 -0.01366  0.97404  0.06831  0.4959   0.50137\n",
      "  0.75683 -0.00273  0.65164 -0.14071  0.40164 -0.48907  0.39208  0.58743\n",
      "  0.76776  0.31831  0.78552  0.11339  0.47541 -0.44945  1.       0.00683\n",
      "  0.60656  0.06967  0.68656  0.17088  0.87568  0.07787  0.55328  0.2459\n",
      "  0.13934  0.48087]  and label  -1  is:  0.03787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.60678 -0.02712  0.67119  0.04068  0.52881 -0.04407\n",
      "  0.50508  0.03729  0.70508 -0.07797  0.57966 -0.02034  0.5322   0.07797\n",
      "  0.64068  0.11864  0.56949 -0.02373  0.5322   0.00678  0.71525 -0.0339\n",
      "  0.52881 -0.0339   0.57262  0.0075   0.58644 -0.00339  0.58983 -0.02712\n",
      "  0.50169  0.0678 ]  and label  1  is:  0.5871212121212122\n",
      "P Value for  [ 1.       0.       0.60678 -0.02712  0.67119  0.04068  0.52881 -0.04407\n",
      "  0.50508  0.03729  0.70508 -0.07797  0.57966 -0.02034  0.5322   0.07797\n",
      "  0.64068  0.11864  0.56949 -0.02373  0.5322   0.00678  0.71525 -0.0339\n",
      "  0.52881 -0.0339   0.57262  0.0075   0.58644 -0.00339  0.58983 -0.02712\n",
      "  0.50169  0.0678 ]  and label  -1  is:  0.007575757575757576\n",
      " \n",
      "P Value for  [ 1.       0.       0.88116  0.27475  0.72125  0.42881  0.61559  0.63662\n",
      "  0.38825  0.90502  0.09831  0.96128 -0.20097  0.892   -0.35737  0.775\n",
      " -0.65114  0.6221  -0.78768  0.45535 -0.81856  0.19095 -0.83943 -0.08079\n",
      " -0.78334 -0.26356 -0.67557 -0.45511 -0.54732 -0.60858 -0.30512 -0.667\n",
      " -0.19312 -0.75597]  and label  1  is:  0.928030303030303\n",
      "P Value for  [ 1.       0.       0.88116  0.27475  0.72125  0.42881  0.61559  0.63662\n",
      "  0.38825  0.90502  0.09831  0.96128 -0.20097  0.892   -0.35737  0.775\n",
      " -0.65114  0.6221  -0.78768  0.45535 -0.81856  0.19095 -0.83943 -0.08079\n",
      " -0.78334 -0.26356 -0.67557 -0.45511 -0.54732 -0.60858 -0.30512 -0.667\n",
      " -0.19312 -0.75597]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.0000e+00  0.0000e+00  8.5736e-01  7.5000e-04  8.1927e-01 -5.6760e-02\n",
      "  7.7521e-01 -4.1820e-02  8.4317e-01  9.0370e-02  8.6258e-01  1.1949e-01\n",
      "  8.8051e-01 -6.1240e-02  7.8342e-01  3.5100e-02  8.3719e-01 -6.7960e-02\n",
      "  8.3570e-01 -1.4190e-01  8.8125e-01  1.1950e-02  9.0515e-01  2.2400e-02\n",
      "  7.9686e-01 -1.9420e-02  8.2383e-01 -3.6780e-02  8.8125e-01 -6.4230e-02\n",
      "  7.3936e-01 -1.9420e-02  7.9089e-01 -9.1860e-02]  and label  1  is:  0.7651515151515151\n",
      "P Value for  [ 1.0000e+00  0.0000e+00  8.5736e-01  7.5000e-04  8.1927e-01 -5.6760e-02\n",
      "  7.7521e-01 -4.1820e-02  8.4317e-01  9.0370e-02  8.6258e-01  1.1949e-01\n",
      "  8.8051e-01 -6.1240e-02  7.8342e-01  3.5100e-02  8.3719e-01 -6.7960e-02\n",
      "  8.3570e-01 -1.4190e-01  8.8125e-01  1.1950e-02  9.0515e-01  2.2400e-02\n",
      "  7.9686e-01 -1.9420e-02  8.2383e-01 -3.6780e-02  8.8125e-01 -6.4230e-02\n",
      "  7.3936e-01 -1.9420e-02  7.9089e-01 -9.1860e-02]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1. -1.\n",
      "  0.  0.  1.  1.  1. -1.  1.  1.  1.  0.  1.  1.  1. -1.  0.  0.]  and label  1  is:  0.21212121212121213\n",
      "P Value for  [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1. -1.\n",
      "  0.  0.  1.  1.  1. -1.  1.  1.  1.  0.  1.  1.  1. -1.  0.  0.]  and label  -1  is:  0.08333333333333333\n",
      " \n",
      "P Value for  [1.      0.      0.91241 0.04347 0.94191 0.0228  0.94705 0.05345 0.93582\n",
      " 0.01321 0.91911 0.06348 0.92766 0.12067 0.92048 0.06211 0.88899 0.12722\n",
      " 0.83744 0.14439 0.80983 0.11849 0.77041 0.14222 0.75755 0.11299 0.7355\n",
      " 0.13282 0.66387 0.153   0.70925 0.10754 0.65258 0.11447]  and label  1  is:  0.7348484848484849\n",
      "P Value for  [1.      0.      0.91241 0.04347 0.94191 0.0228  0.94705 0.05345 0.93582\n",
      " 0.01321 0.91911 0.06348 0.92766 0.12067 0.92048 0.06211 0.88899 0.12722\n",
      " 0.83744 0.14439 0.80983 0.11849 0.77041 0.14222 0.75755 0.11299 0.7355\n",
      " 0.13282 0.66387 0.153   0.70925 0.10754 0.65258 0.11447]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.61857  0.1085   0.70694 -0.06935  0.70358  0.01678\n",
      "  0.74273  0.00224  0.71029  0.15772  0.71588 -0.00224  0.79754  0.066\n",
      "  0.83669 -0.16555  0.6868  -0.0906   0.62528 -0.01342  0.60962  0.11745\n",
      "  0.71253 -0.09508  0.69845 -0.01673  0.63311  0.0481   0.78859 -0.05145\n",
      "  0.65213 -0.04698]  and label  1  is:  0.6439393939393939\n",
      "P Value for  [ 1.       0.       0.61857  0.1085   0.70694 -0.06935  0.70358  0.01678\n",
      "  0.74273  0.00224  0.71029  0.15772  0.71588 -0.00224  0.79754  0.066\n",
      "  0.83669 -0.16555  0.6868  -0.0906   0.62528 -0.01342  0.60962  0.11745\n",
      "  0.71253 -0.09508  0.69845 -0.01673  0.63311  0.0481   0.78859 -0.05145\n",
      "  0.65213 -0.04698]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       1.      -0.08183  1.      -0.11326  0.99246 -0.29802\n",
      "  1.      -0.33075  0.96662 -0.34281  0.85788 -0.47265  0.91904 -0.4817\n",
      "  0.73084 -0.65224  0.68131 -0.63544  0.8245  -0.78316  0.58829 -0.74785\n",
      "  0.67033 -0.96296  0.48757 -0.85669  0.37941 -0.83893  0.24117 -0.88846\n",
      "  0.29221 -0.89621]  and label  1  is:  0.8484848484848485\n",
      "P Value for  [ 1.       0.       1.      -0.08183  1.      -0.11326  0.99246 -0.29802\n",
      "  1.      -0.33075  0.96662 -0.34281  0.85788 -0.47265  0.91904 -0.4817\n",
      "  0.73084 -0.65224  0.68131 -0.63544  0.8245  -0.78316  0.58829 -0.74785\n",
      "  0.67033 -0.96296  0.48757 -0.85669  0.37941 -0.83893  0.24117 -0.88846\n",
      "  0.29221 -0.89621]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.79378  0.29492  0.64064  0.52312  0.41319  0.68158\n",
      "  0.14177  0.83548 -0.16831  0.78772 -0.42911  0.72328 -0.57165  0.41471\n",
      " -0.75436  0.16755 -0.69977 -0.09856 -0.57695 -0.23503 -0.40637 -0.38287\n",
      " -0.17437 -0.5254   0.01523 -0.48707  0.1903  -0.38059  0.31008 -0.23199\n",
      "  0.34572 -0.08036]  and label  1  is:  0.7954545454545454\n",
      "P Value for  [ 1.       0.       0.79378  0.29492  0.64064  0.52312  0.41319  0.68158\n",
      "  0.14177  0.83548 -0.16831  0.78772 -0.42911  0.72328 -0.57165  0.41471\n",
      " -0.75436  0.16755 -0.69977 -0.09856 -0.57695 -0.23503 -0.40637 -0.38287\n",
      " -0.17437 -0.5254   0.01523 -0.48707  0.1903  -0.38059  0.31008 -0.23199\n",
      "  0.34572 -0.08036]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.      0.      0.      0.     -1.     -1.     -1.      1.      0.\n",
      "  0.     -1.      1.      1.      1.      1.     -1.      0.      0.\n",
      "  0.      0.     -1.     -1.     -1.      1.      1.      0.4375  1.\n",
      " -1.      0.      0.     -1.     -1.     -1.      1.    ]  and label  1  is:  0.06818181818181818\n",
      "P Value for  [ 1.      0.      0.      0.     -1.     -1.     -1.      1.      0.\n",
      "  0.     -1.      1.      1.      1.      1.     -1.      0.      0.\n",
      "  0.      0.     -1.     -1.     -1.      1.      1.      0.4375  1.\n",
      " -1.      0.      0.     -1.     -1.     -1.      1.    ]  and label  -1  is:  0.2537878787878788\n",
      " \n",
      "P Value for  [ 1.  0.  1. -1.  1.  1. -1.  1.  1. -1.  1. -1. -1. -1. -1.  1.  1.  1.\n",
      "  1.  1. -1.  1.  1. -1.  1. -1.  1.  1.  1.  1. -1.  1. -1.  1.]  and label  1  is:  0.06439393939393939\n",
      "P Value for  [ 1.  0.  1. -1.  1.  1. -1.  1.  1. -1.  1. -1. -1. -1. -1.  1.  1.  1.\n",
      "  1.  1. -1.  1.  1. -1.  1. -1.  1.  1.  1.  1. -1.  1. -1.  1.]  and label  -1  is:  0.2537878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       1.      -0.03365  1.       0.00485  1.      -0.12062\n",
      "  0.88965  0.01198  0.73082  0.05346  0.85443  0.00827  0.54591  0.00299\n",
      "  0.83775 -0.13644  0.75535 -0.0854   0.70887 -0.27502  0.43385 -0.12062\n",
      "  0.57528 -0.4022   0.58984 -0.22145  0.431   -0.17365  0.60436 -0.2418\n",
      "  0.56045 -0.38238]  and label  1  is:  0.3333333333333333\n",
      "P Value for  [ 1.       0.       1.      -0.03365  1.       0.00485  1.      -0.12062\n",
      "  0.88965  0.01198  0.73082  0.05346  0.85443  0.00827  0.54591  0.00299\n",
      "  0.83775 -0.13644  0.75535 -0.0854   0.70887 -0.27502  0.43385 -0.12062\n",
      "  0.57528 -0.4022   0.58984 -0.22145  0.431   -0.17365  0.60436 -0.2418\n",
      "  0.56045 -0.38238]  and label  -1  is:  0.03787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.44444  0.44444  0.53695  0.90763 -0.22222  1.\n",
      " -0.33333  0.88889 -1.       0.33333 -1.      -0.11111 -1.      -0.22222\n",
      " -0.66667 -0.77778  0.55556 -1.      -0.22222 -0.77778  0.77778 -0.22222\n",
      "  0.33333  0.       0.9212   0.45019  0.57454  0.84353  0.22222  1.\n",
      " -0.55556  1.     ]  and label  1  is:  0.6439393939393939\n",
      "P Value for  [ 1.       0.       0.44444  0.44444  0.53695  0.90763 -0.22222  1.\n",
      " -0.33333  0.88889 -1.       0.33333 -1.      -0.11111 -1.      -0.22222\n",
      " -0.66667 -0.77778  0.55556 -1.      -0.22222 -0.77778  0.77778 -0.22222\n",
      "  0.33333  0.       0.9212   0.45019  0.57454  0.84353  0.22222  1.\n",
      " -0.55556  1.     ]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.80627  0.13069  0.73061  0.24323  0.64615  0.19038\n",
      "  0.36923  0.45577  0.44793  0.46439  0.25     0.57308  0.25192  0.37115\n",
      "  0.15215  0.51877 -0.09808  0.575   -0.03462  0.42885 -0.08856  0.44424\n",
      " -0.14943  0.40006 -0.1994   0.34976 -0.23832  0.29541 -0.26634  0.23896\n",
      " -0.23846  0.31154]  and label  1  is:  0.32196969696969696\n",
      "P Value for  [ 1.       0.       0.80627  0.13069  0.73061  0.24323  0.64615  0.19038\n",
      "  0.36923  0.45577  0.44793  0.46439  0.25     0.57308  0.25192  0.37115\n",
      "  0.15215  0.51877 -0.09808  0.575   -0.03462  0.42885 -0.08856  0.44424\n",
      " -0.14943  0.40006 -0.1994   0.34976 -0.23832  0.29541 -0.26634  0.23896\n",
      " -0.23846  0.31154]  and label  -1  is:  0.04924242424242424\n",
      " \n",
      "P Value for  [ 1.       0.       0.92247 -0.19448  0.96419 -0.17674  0.87024 -0.22602\n",
      "  0.81702 -0.2707   0.79271 -0.28909  0.70302 -0.49639  0.63338 -0.49967\n",
      "  0.37254 -0.70729  0.2707  -0.72109  0.40506 -0.54172  0.33509 -0.59691\n",
      "  0.1475  -0.63601  0.09312 -0.59589 -0.07162 -0.54928 -0.0184  -0.54074\n",
      " -0.07457 -0.47898]  and label  1  is:  0.6553030303030303\n",
      "P Value for  [ 1.       0.       0.92247 -0.19448  0.96419 -0.17674  0.87024 -0.22602\n",
      "  0.81702 -0.2707   0.79271 -0.28909  0.70302 -0.49639  0.63338 -0.49967\n",
      "  0.37254 -0.70729  0.2707  -0.72109  0.40506 -0.54172  0.33509 -0.59691\n",
      "  0.1475  -0.63601  0.09312 -0.59589 -0.07162 -0.54928 -0.0184  -0.54074\n",
      " -0.07457 -0.47898]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.88103 -0.00857  0.89818 -0.02465  0.94105 -0.01822\n",
      "  0.89175 -0.12755  0.82208 -0.10932  0.88853  0.01179  0.90782 -0.13719\n",
      "  0.87138 -0.06109  0.90782 -0.02358  0.87996 -0.14577  0.82851 -0.12433\n",
      "  0.90139 -0.19507  0.88245 -0.14903  0.84352 -0.12862  0.88424 -0.18542\n",
      "  0.91747 -0.16827]  and label  1  is:  0.7310606060606061\n",
      "P Value for  [ 1.       0.       0.88103 -0.00857  0.89818 -0.02465  0.94105 -0.01822\n",
      "  0.89175 -0.12755  0.82208 -0.10932  0.88853  0.01179  0.90782 -0.13719\n",
      "  0.87138 -0.06109  0.90782 -0.02358  0.87996 -0.14577  0.82851 -0.12433\n",
      "  0.90139 -0.19507  0.88245 -0.14903  0.84352 -0.12862  0.88424 -0.18542\n",
      "  0.91747 -0.16827]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.95217  0.06595  0.93614  0.1303   0.90996  0.19152\n",
      "  0.84881 -0.49962  0.90023  0.6132   0.77937  0.34328  0.72254  0.37988\n",
      "  0.66145  0.40844  0.95472  0.59862  0.53258  0.44088  0.46773  0.44511\n",
      "  0.4044   0.44199  0.34374  0.43221  0.9033   1.       0.23405  0.3962\n",
      "  0.18632  0.37191]  and label  1  is:  0.44696969696969696\n",
      "P Value for  [ 1.       0.       0.95217  0.06595  0.93614  0.1303   0.90996  0.19152\n",
      "  0.84881 -0.49962  0.90023  0.6132   0.77937  0.34328  0.72254  0.37988\n",
      "  0.66145  0.40844  0.95472  0.59862  0.53258  0.44088  0.46773  0.44511\n",
      "  0.4044   0.44199  0.34374  0.43221  0.9033   1.       0.23405  0.3962\n",
      "  0.18632  0.37191]  and label  -1  is:  0.015151515151515152\n",
      " \n",
      "P Value for  [ 1.       0.       0.97714  0.19049  0.82683  0.46259  0.71771  0.58732\n",
      "  0.47968  0.84278  0.31409  0.92643  0.10289  0.93945 -0.13254  0.8429\n",
      " -0.3202   0.91624 -0.52145  0.79525 -0.68274  0.49508 -0.77408  0.33537\n",
      " -0.85376  0.17849 -0.83314 -0.01358 -0.82366 -0.19321 -0.67289 -0.33662\n",
      " -0.59943 -0.497  ]  and label  1  is:  0.8181818181818182\n",
      "P Value for  [ 1.       0.       0.97714  0.19049  0.82683  0.46259  0.71771  0.58732\n",
      "  0.47968  0.84278  0.31409  0.92643  0.10289  0.93945 -0.13254  0.8429\n",
      " -0.3202   0.91624 -0.52145  0.79525 -0.68274  0.49508 -0.77408  0.33537\n",
      " -0.85376  0.17849 -0.83314 -0.01358 -0.82366 -0.19321 -0.67289 -0.33662\n",
      " -0.59943 -0.497  ]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.94598 -0.02685 -1.       0.26131 -0.36393  0.35639\n",
      "  0.69258 -0.63427  1.      -0.03353 -0.2902  -0.0055  -0.54852  0.15452\n",
      "  0.91921 -0.4627   1.      -0.50424 -0.29735 -0.31454 -0.73864  0.37361\n",
      "  0.83872 -0.46734  0.52208 -0.5813   1.      -0.61393 -0.09634  0.20477\n",
      " -0.06117  0.41913]  and label  1  is:  0.03787878787878788\n",
      "P Value for  [ 1.       0.       0.94598 -0.02685 -1.       0.26131 -0.36393  0.35639\n",
      "  0.69258 -0.63427  1.      -0.03353 -0.2902  -0.0055  -0.54852  0.15452\n",
      "  0.91921 -0.4627   1.      -0.50424 -0.29735 -0.31454 -0.73864  0.37361\n",
      "  0.83872 -0.46734  0.52208 -0.5813   1.      -0.61393 -0.09634  0.20477\n",
      " -0.06117  0.41913]  and label  -1  is:  0.35984848484848486\n",
      " \n",
      "P Value for  [ 1.       0.       1.       1.       0.367    0.06158  0.12993  0.92713\n",
      " -0.27586  0.93596 -0.31527  0.37685 -0.87192  0.36946 -0.92857 -0.08867\n",
      " -0.38916 -0.34236 -0.46552 -0.82512 -0.05419 -0.93596  0.25616 -0.20443\n",
      "  0.73792 -0.4595   0.85471 -0.06831  1.       1.       0.3867   0.00246\n",
      "  0.17758  0.7979 ]  and label  1  is:  0.6287878787878788\n",
      "P Value for  [ 1.       0.       1.       1.       0.367    0.06158  0.12993  0.92713\n",
      " -0.27586  0.93596 -0.31527  0.37685 -0.87192  0.36946 -0.92857 -0.08867\n",
      " -0.38916 -0.34236 -0.46552 -0.82512 -0.05419 -0.93596  0.25616 -0.20443\n",
      "  0.73792 -0.4595   0.85471 -0.06831  1.       1.       0.3867   0.00246\n",
      "  0.17758  0.7979 ]  and label  -1  is:  0.007575757575757576\n",
      " \n",
      "P Value for  [ 1.0000e+00  0.0000e+00  6.4947e-01 -7.8960e-02  5.8264e-01 -1.4380e-01\n",
      " -1.3129e-01 -2.1384e-01  2.9796e-01  4.4030e-02  3.8096e-01 -2.6339e-01\n",
      "  2.8931e-01 -3.1997e-01  3.4590e-02 -1.8947e-01  2.0269e-01 -2.9441e-01\n",
      "  1.5196e-01 -2.9052e-01  9.5130e-02 -3.1525e-01  6.5560e-02 -2.6795e-01\n",
      "  3.0040e-02 -2.5124e-01 -4.6000e-04 -2.3210e-01 -2.6120e-02 -2.1129e-01\n",
      " -4.7170e-02 -1.8950e-01  1.3360e-02 -2.7201e-01]  and label  1  is:  0.36742424242424243\n",
      "P Value for  [ 1.0000e+00  0.0000e+00  6.4947e-01 -7.8960e-02  5.8264e-01 -1.4380e-01\n",
      " -1.3129e-01 -2.1384e-01  2.9796e-01  4.4030e-02  3.8096e-01 -2.6339e-01\n",
      "  2.8931e-01 -3.1997e-01  3.4590e-02 -1.8947e-01  2.0269e-01 -2.9441e-01\n",
      "  1.5196e-01 -2.9052e-01  9.5130e-02 -3.1525e-01  6.5560e-02 -2.6795e-01\n",
      "  3.0040e-02 -2.5124e-01 -4.6000e-04 -2.3210e-01 -2.6120e-02 -2.1129e-01\n",
      " -4.7170e-02 -1.8950e-01  1.3360e-02 -2.7201e-01]  and label  -1  is:  0.03409090909090909\n",
      " \n",
      "P Value for  [ 1.       0.       0.87032  0.46972  0.53945  0.82161  0.1038   0.95275\n",
      " -0.38033  0.87916 -0.73939  0.58226 -0.92099  0.16731 -0.82417 -0.24942\n",
      " -0.59383 -0.63342 -0.24012 -0.82881  0.18823 -0.78699  0.51557 -0.5743\n",
      "  0.69274 -0.24843  0.69097  0.10484  0.52798  0.39762  0.25974  0.56573\n",
      " -0.06739  0.57552]  and label  1  is:  0.9583333333333334\n",
      "P Value for  [ 1.       0.       0.87032  0.46972  0.53945  0.82161  0.1038   0.95275\n",
      " -0.38033  0.87916 -0.73939  0.58226 -0.92099  0.16731 -0.82417 -0.24942\n",
      " -0.59383 -0.63342 -0.24012 -0.82881  0.18823 -0.78699  0.51557 -0.5743\n",
      "  0.69274 -0.24843  0.69097  0.10484  0.52798  0.39762  0.25974  0.56573\n",
      " -0.06739  0.57552]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.0000e+00  0.0000e+00  3.6876e-01 -1.0000e+00 -1.0000e+00 -1.0000e+00\n",
      " -7.6610e-02  1.0000e+00  1.0000e+00  9.5041e-01  7.4597e-01 -3.8710e-01\n",
      " -1.0000e+00 -7.9313e-01 -9.6770e-02  1.0000e+00  4.8684e-01  4.6502e-01\n",
      "  3.1755e-01 -2.7461e-01 -1.4343e-01 -2.0188e-01 -1.1976e-01  6.8950e-02\n",
      "  3.0210e-02  6.6390e-02  3.4430e-02 -1.1860e-02 -4.0300e-03 -1.6720e-02\n",
      " -7.6100e-03  1.0800e-03  1.5000e-04  3.2500e-03]  and label  1  is:  0.10227272727272728\n",
      "P Value for  [ 1.0000e+00  0.0000e+00  3.6876e-01 -1.0000e+00 -1.0000e+00 -1.0000e+00\n",
      " -7.6610e-02  1.0000e+00  1.0000e+00  9.5041e-01  7.4597e-01 -3.8710e-01\n",
      " -1.0000e+00 -7.9313e-01 -9.6770e-02  1.0000e+00  4.8684e-01  4.6502e-01\n",
      "  3.1755e-01 -2.7461e-01 -1.4343e-01 -2.0188e-01 -1.1976e-01  6.8950e-02\n",
      "  3.0210e-02  6.6390e-02  3.4430e-02 -1.1860e-02 -4.0300e-03 -1.6720e-02\n",
      " -7.6100e-03  1.0800e-03  1.5000e-04  3.2500e-03]  and label  -1  is:  0.1590909090909091\n",
      " \n",
      "P Value for  [ 1.       0.       1.       1.       1.      -1.       0.       0.\n",
      "  0.       0.       1.       1.       1.      -1.       1.       1.\n",
      "  1.      -1.       0.       0.       0.       0.       1.      -0.27778\n",
      "  0.       0.       1.      -1.       1.       1.       1.      -1.\n",
      "  0.       0.     ]  and label  1  is:  0.030303030303030304\n",
      "P Value for  [ 1.       0.       1.       1.       1.      -1.       0.       0.\n",
      "  0.       0.       1.       1.       1.      -1.       1.       1.\n",
      "  1.      -1.       0.       0.       0.       0.       1.      -0.27778\n",
      "  0.       0.       1.      -1.       1.       1.       1.      -1.\n",
      "  0.       0.     ]  and label  -1  is:  0.39015151515151514\n",
      " \n",
      "P Value for  [ 0.  0. -1. -1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1. -1.  0.  0.\n",
      " -1. -1.  0.  0.  1.  1.  1. -1.  1. -1.  0.  0.  0.  0.  0.  0.]  and label  1  is:  0.03787878787878788\n",
      "P Value for  [ 0.  0. -1. -1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1. -1.  0.  0.\n",
      " -1. -1.  0.  0.  1.  1.  1. -1.  1. -1.  0.  0.  0.  0.  0.  0.]  and label  -1  is:  0.3333333333333333\n",
      " \n",
      "P Value for  [ 1.       0.       0.99645  0.06468  1.      -0.01236  0.97811  0.02498\n",
      "  0.96112  0.02312  0.99274  0.07808  0.89323  0.10346  0.94212  0.05269\n",
      "  0.88809  0.1112   0.86104  0.08631  0.81633  0.1183   0.83668  0.14442\n",
      "  0.81329  0.13412  0.79476  0.13638  0.7911   0.15379  0.77122  0.1593\n",
      "  0.70941  0.12015]  and label  1  is:  0.7954545454545454\n",
      "P Value for  [ 1.       0.       0.99645  0.06468  1.      -0.01236  0.97811  0.02498\n",
      "  0.96112  0.02312  0.99274  0.07808  0.89323  0.10346  0.94212  0.05269\n",
      "  0.88809  0.1112   0.86104  0.08631  0.81633  0.1183   0.83668  0.14442\n",
      "  0.81329  0.13412  0.79476  0.13638  0.7911   0.15379  0.77122  0.1593\n",
      "  0.70941  0.12015]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.0000e+00  0.0000e+00  2.9073e-01  1.0025e-01  2.3308e-01  1.7293e-01\n",
      "  3.7590e-02  3.4336e-01  1.2030e-01  2.6316e-01  6.2660e-02  2.1303e-01\n",
      " -4.7250e-02  1.2767e-01 -6.3330e-02  7.9070e-02 -6.3280e-02  4.0970e-02\n",
      " -5.4310e-02  1.4080e-02 -4.1660e-02 -2.8000e-03 -2.8760e-02 -1.1760e-02\n",
      " -1.7550e-02 -1.5050e-02 -8.8600e-03 -1.4750e-02 -2.8000e-03 -1.2500e-02\n",
      "  9.6000e-04 -9.4800e-03  2.9000e-03 -6.4700e-03]  and label  1  is:  0.06439393939393939\n",
      "P Value for  [ 1.0000e+00  0.0000e+00  2.9073e-01  1.0025e-01  2.3308e-01  1.7293e-01\n",
      "  3.7590e-02  3.4336e-01  1.2030e-01  2.6316e-01  6.2660e-02  2.1303e-01\n",
      " -4.7250e-02  1.2767e-01 -6.3330e-02  7.9070e-02 -6.3280e-02  4.0970e-02\n",
      " -5.4310e-02  1.4080e-02 -4.1660e-02 -2.8000e-03 -2.8760e-02 -1.1760e-02\n",
      " -1.7550e-02 -1.5050e-02 -8.8600e-03 -1.4750e-02 -2.8000e-03 -1.2500e-02\n",
      "  9.6000e-04 -9.4800e-03  2.9000e-03 -6.4700e-03]  and label  -1  is:  0.2878787878787879\n",
      " \n",
      "P Value for  [ 1.       0.       0.74084  0.04974  0.79074  0.02543  0.78575  0.03793\n",
      "  0.6623   0.09948  0.67801  0.31152  0.75934  0.07348  0.74695  0.08442\n",
      "  0.70681 -0.07853  0.63613  0.       0.70021  0.11355  0.68183  0.12185\n",
      "  0.67016  0.15445  0.64158  0.13608  0.65707  0.17539  0.59759  0.14697\n",
      "  0.57455  0.15114]  and label  1  is:  0.678030303030303\n",
      "P Value for  [ 1.       0.       0.74084  0.04974  0.79074  0.02543  0.78575  0.03793\n",
      "  0.6623   0.09948  0.67801  0.31152  0.75934  0.07348  0.74695  0.08442\n",
      "  0.70681 -0.07853  0.63613  0.       0.70021  0.11355  0.68183  0.12185\n",
      "  0.67016  0.15445  0.64158  0.13608  0.65707  0.17539  0.59759  0.14697\n",
      "  0.57455  0.15114]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.90374 -0.01604  1.       0.08021  1.       0.01604\n",
      "  0.93048  0.00535  0.93583 -0.01604  1.       0.       1.       0.06417\n",
      "  1.       0.04813  0.91444  0.04278  0.96791  0.02139  0.9893  -0.01604\n",
      "  0.96257  0.05348  0.96974  0.04452  0.87701  0.0107   1.       0.09091\n",
      "  0.97861  0.06417]  and label  1  is:  0.928030303030303\n",
      "P Value for  [ 1.       0.       0.90374 -0.01604  1.       0.08021  1.       0.01604\n",
      "  0.93048  0.00535  0.93583 -0.01604  1.       0.       1.       0.06417\n",
      "  1.       0.04813  0.91444  0.04278  0.96791  0.02139  0.9893  -0.01604\n",
      "  0.96257  0.05348  0.96974  0.04452  0.87701  0.0107   1.       0.09091\n",
      "  0.97861  0.06417]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.       0.       0.79157  0.16851  0.       0.       0.56541  0.06874\n",
      "  0.39468  1.       0.38359  0.99557 -0.02439  0.53215  0.23725  0.1286\n",
      " -0.02661  0.95122 -0.50998  0.84922 -0.102    0.38803 -0.42572  0.23725\n",
      " -0.91574  0.8071  -0.34146  0.88248 -1.       0.69401 -1.       0.1286\n",
      "  0.       0.     ]  and label  1  is:  0.4393939393939394\n",
      "P Value for  [ 1.       0.       0.79157  0.16851  0.       0.       0.56541  0.06874\n",
      "  0.39468  1.       0.38359  0.99557 -0.02439  0.53215  0.23725  0.1286\n",
      " -0.02661  0.95122 -0.50998  0.84922 -0.102    0.38803 -0.42572  0.23725\n",
      " -0.91574  0.8071  -0.34146  0.88248 -1.       0.69401 -1.       0.1286\n",
      "  0.       0.     ]  and label  -1  is:  0.01893939393939394\n",
      " \n",
      "P Value for  [ 1.       0.       0.97905  0.1581   0.90112  0.35237  0.82039  0.48561\n",
      "  0.7176   0.64888  0.58827  0.73743  0.40349  0.83156  0.2514   0.84804\n",
      "  0.047    0.85475 -0.12193  0.79749 -0.2618   0.80754 -0.37835  0.71676\n",
      " -0.51034  0.58324 -0.57587  0.4604  -0.61899  0.30796 -0.65754  0.18345\n",
      " -0.64134  0.02968]  and label  1  is:  0.9204545454545454\n",
      "P Value for  [ 1.       0.       0.97905  0.1581   0.90112  0.35237  0.82039  0.48561\n",
      "  0.7176   0.64888  0.58827  0.73743  0.40349  0.83156  0.2514   0.84804\n",
      "  0.047    0.85475 -0.12193  0.79749 -0.2618   0.80754 -0.37835  0.71676\n",
      " -0.51034  0.58324 -0.57587  0.4604  -0.61899  0.30796 -0.65754  0.18345\n",
      " -0.64134  0.02968]  and label  -1  is:  0.003787878787878788\n",
      " \n",
      "P Value for  [ 1.    0.    0.42 -0.61  0.    0.    1.   -1.    0.9   1.    0.43  0.64\n",
      "  0.    0.    0.    0.    0.67 -0.29  0.84 -1.    0.    0.    0.    0.\n",
      "  0.21  0.68  1.    0.22  0.    0.    0.    0.    0.    0.  ]  and label  1  is:  0.022727272727272728\n",
      "P Value for  [ 1.    0.    0.42 -0.61  0.    0.    1.   -1.    0.9   1.    0.43  0.64\n",
      "  0.    0.    0.    0.    0.67 -0.29  0.84 -1.    0.    0.    0.    0.\n",
      "  0.21  0.68  1.    0.22  0.    0.    0.    0.    0.    0.  ]  and label  -1  is:  0.4318181818181818\n",
      " \n",
      "P Value for  [ 1.       0.       1.       0.0738   1.       0.0342   1.      -0.05563\n",
      "  1.       0.08764  1.       0.19651  1.       0.20328  1.       0.12785\n",
      "  1.       0.10561  1.       0.27087  1.       0.44758  1.       0.4175\n",
      "  1.       0.20033  1.       0.36743  0.95603  0.48641  1.       0.32492\n",
      "  1.       0.46712]  and label  1  is:  0.5833333333333334\n",
      "P Value for  [ 1.       0.       1.       0.0738   1.       0.0342   1.      -0.05563\n",
      "  1.       0.08764  1.       0.19651  1.       0.20328  1.       0.12785\n",
      "  1.       0.10561  1.       0.27087  1.       0.44758  1.       0.4175\n",
      "  1.       0.20033  1.       0.36743  0.95603  0.48641  1.       0.32492\n",
      "  1.       0.46712]  and label  -1  is:  0.007575757575757576\n",
      " \n",
      "The Average False P-Value of Iono is:  0.05436466942148768\n"
     ]
    }
   ],
   "source": [
    "print(\"The Average False P-Value of Iono is: \" ,calculateAvgFalseP(ionoX_train,ionoY_train, ionoX_test,ionoY_test))"
   ]
  }
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