for_paper.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import itertools as it\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pylab as pl\n",
    "from IPython import display\n",
    "\n",
    "from Node.functions import *\n",
    "from Node.Node import Control_node\n",
    "from Plant.Plant import Plant"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single 2D Agent\n",
    "\n",
    "Hitting a static target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# behavioral matrix (maps previous behavior to future behavior commands - how should I behave?)\n",
    "# behavioral matrix (maps previous behavior to future behavior commands - how should I behave?)\n",
    "B1 = np.array([[1]])\n",
    "\n",
    "# state/system matrix (maps previous state to future state - how will others behave (give past behaviors)?)\n",
    "A1 = np.array([[1]])\n",
    "\n",
    "# reference matrix (how do I expect/want others to behave? target values)\n",
    "R1 = np.array([0.0])\n",
    "\n",
    "# Initial behavior\n",
    "behav_1 = np.array([2.2])\n",
    "\n",
    "# transition matrix (maps control signal and previous state to current state - what behaviors do I enact given my intentions?)\n",
    "T1 = np.array([0.1])\n",
    "\n",
    "\n",
    "\n",
    "# get the state observation (behaviors of others)\n",
    "def sense(inputs):\n",
    "    return inputs\n",
    "\n",
    "# contrast reference with observations (and potentially incorporate predictions from internal model)\n",
    "def compare(sensory_signal, reference, prediction):\n",
    "    error = reference - sensory_signal\n",
    "    return error\n",
    "\n",
    "# generate a behavior\n",
    "def control(error, behavioral_model, previous_output):\n",
    "    #output = np.array([behavioral_model[i][i]*error[i] for i in range(len(error))])\n",
    "    output = behavioral_model @ error\n",
    "    return output\n",
    "\n",
    "\n",
    "\n",
    "# does not adjust reference\n",
    "def reference_update_false(reference, error):\n",
    "    k = 0\n",
    "    reference = reference + (error * k)\n",
    "    return reference\n",
    "\n",
    "# dictates how control commands determine state transitions - i.e. behaviors\n",
    "def transition(control_signal, transition_matrix, state):\n",
    "    new_state = (transition_matrix * control_signal) + state\n",
    "    return new_state\n",
    "\n",
    "\n",
    "# adjusts behavioral model to get closer to behavior given \n",
    "def control_update(error, behavioral_model, previous_output):\n",
    "    # LMS algorithm\n",
    "    learning_rate = 0.0\n",
    "    #behavioral_model = behavioral_model + learning_rate * error * previous_output\n",
    "    return behavioral_model\n",
    "\n",
    "# generate predictions on next state\n",
    "def imc(system_estimate, previous_state, behavioral_model, previous_output):\n",
    "    # actual estimate\n",
    "    state = (system_estimate * previous_state) + (behavioral_model * previous_output)\n",
    "    return state\n",
    "\n",
    "# adjust system estimate to infer observations given my behavior\n",
    "def imc_update(inputs, system_estimate, previous_behavior, behavioral_model, previous_state, learning_rate):\n",
    "    # actual estimate\n",
    "    #curr_system_est = (system_estimate @ previous_state) + (behavioral_model @ previous_behavior)\n",
    "    #error = curr_system_est - inputs\n",
    "    #learning_rate = 0.01\n",
    "    #system_estimate = system_estimate + learning_rate * error * previous_behavior\n",
    "    return system_estimate\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# agent two acts as the plant (error = control output)\n",
    "def plant_control(control_signal, transition_matrix, state):\n",
    "    output = control_signal*transition_matrix + state\n",
    "    return output\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "agent1 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_false,\n",
    "controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "plant1 = Plant(transition_matrix=T1, transition_function=plant_control, state=behav_1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "trials = 50\n",
    "ticks = [n for n in range(trials)]\n",
    "color = (0.1,0.2,0.8,0.1)\n",
    "behavior1 = np.array([0,0])\n",
    "controller_error = []\n",
    "agent1_behaviors = []\n",
    "plant_outputs = []\n",
    "\n",
    "state = np.array([2.5])\n",
    "agent1_error = []\n",
    "#hfont = {'fontname':'SuperClarendon'}\n",
    "#plt.scatter(R1[0],R1[1], c=(0.1,0.1,0.8), edgecolors=[0.8,0.1,0.1], linewidths=1.5)\n",
    "for t in range(trials):\n",
    "    color1 = [0.1,0.2,0.8, (t+1)/(trials+1)]\n",
    "    b1 = agent1.go(state)   \n",
    "    plant_output = plant1.transition(b1)\n",
    "\n",
    "    agent1_behaviors.append(agent1.get_output())\n",
    "    plant_outputs.append(plant_output)\n",
    "    controller_error.append(agent1.get_error())\n",
    "    agent1_error.append(sum(abs(agent1.get_error())/len(agent1.get_error())))\n",
    "\n",
    "    behavior1 = b1\n",
    "    state = plant_output\n",
    "    \n",
    "    color = [0.1,0.1,0.8, (t+1)/(trials+1)]\n",
    "    plt.scatter(plant_output[0],(t+1)/(trials+1),c=tuple(color1))\n",
    "    pl.scatter(agent1.get_reference()[0],(t+1)/(trials+1),c=tuple(color1), edgecolors=[0.8,0.1,0.1, color1[3]], linewidth=1.5)\n",
    "    plt.legend([[agent1.get_reference()[0], 0], state], labels=[\"Reference Signal\", \"Agent Behavior\"])\n",
    "    lg = plt.gca().get_legend()\n",
    "    lg.legendHandles[0].set_color((0.1,0.1,0.8))\n",
    "    lg.legendHandles[0].set_edgecolor((0.8,0.1,0.1))\n",
    "    lg.legendHandles[1].set_color((0.1,0.1,0.8))\n",
    "    display.clear_output(wait=True)\n",
    "    plt.title(\"Behavior over Time\", weight='bold', fontsize=24, pad=24)\n",
    "    plt.xlabel(\"Behavior Value (X Position)\", fontsize=16)\n",
    "    plt.ylabel(\"Time Step\", fontsize=16)\n",
    "    display.display(plt.gcf())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The three strategies\n",
    "\n",
    "conformist x conformist in blue\n",
    "\n",
    "stubborn x conformist in green\n",
    "\n",
    "mixed/sigmoid x conformist in red"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seed was: 22\n"
     ]
    }
   ],
   "source": [
    "# seed = random.randrange(1000)\n",
    "seed = 22\n",
    "np.random.seed(seed)\n",
    "print(\"Seed was:\", seed)\n",
    "\n",
    "behav_size = 1\n",
    "sigmoid_k = 200000000000\n",
    "sigmoid_b = 40\n",
    "# behavioral matrix (maps previous behavior to future behavior commands - how should I behave?)\n",
    "# behavioral matrix (maps previous behavior to future behavior commands - how should I behave?)\n",
    "B1 = np.random.rand(1, behav_size)\n",
    "B2 = np.random.rand(1, behav_size)\n",
    "\n",
    "# state/system matrix (maps previous state to future state - how will others behave (give past behaviors)?)\n",
    "A1 = np.random.rand(1, behav_size)\n",
    "A2 = np.random.rand(1, behav_size)\n",
    "\n",
    "# reference matrix (how do I expect/want others to behave? target values)\n",
    "R1 = np.random.rand(1, behav_size)\n",
    "R2 = np.random.rand(1, behav_size)\n",
    "\n",
    "# Initial behavior\n",
    "behav_1 = np.random.rand(1, behav_size)\n",
    "behav_2 = np.random.rand(1, behav_size)\n",
    "\n",
    "# transition matrix (maps control signal and previous state to current state - what behaviors do I enact given my intentions?)\n",
    "T1 = np.array([0.2])\n",
    "T2 = np.array([0.2])\n",
    "\n",
    "color2 = [0.1,0.6,0.2, 0.7]\n",
    "color2c = [0.2,0.9,0.3, 0.4]\n",
    "\n",
    "# get the state observation (behaviors of others)\n",
    "def sense(inputs):\n",
    "    perception = inputs\n",
    "    return perception\n",
    "\n",
    "# contrast reference with observations (and potentially incorporate predictions from internal model)\n",
    "def compare(sensory_signal, reference, prediction):\n",
    "    error = reference - sensory_signal\n",
    "    return error\n",
    "\n",
    "# generate a control signal\n",
    "def control(error, behavioral_model, previous_output):\n",
    "    control_signal = chaotic_controller(behavioral_model, 0.01, error)\n",
    "    return control_signal\n",
    "\n",
    "# dictates how control commands determine state transitions - i.e. behaviors\n",
    "def transition(control_signal, transition_matrix, state):\n",
    "    new_state = (transition_matrix * control_signal) + state\n",
    "    new_state[new_state > 1] = 1\n",
    "    new_state[new_state < 0] = 0\n",
    "    return new_state\n",
    "\n",
    "\n",
    "# adjusts behavioral model to get closer to behavior given \n",
    "def control_update(error, behavioral_model, previous_output):\n",
    "    # LMS algorithm\n",
    "    learning_rate = 0.0\n",
    "    #behavioral_model = behavioral_model + learning_rate * error * previous_output\n",
    "    return behavioral_model\n",
    "\n",
    "# adjusts reference given error\n",
    "def reference_update_true(reference, error):\n",
    "    k = 0.2\n",
    "    reference = reference - (error * k)\n",
    "    reference[reference > 1] = 1\n",
    "    reference[reference < 0] = 0\n",
    "    return reference\n",
    "\n",
    "# does not adjust reference\n",
    "def reference_update_false(reference, error):\n",
    "    k = 0\n",
    "    reference = reference + (error * k)\n",
    "    return reference\n",
    "\n",
    "def reference_update_sigmoid(reference, error):\n",
    "    # reference = (matrix_sigmoid(error)-1)*(R1) + matrix_sigmoid(error)*(reference + error)\n",
    "    sqr_error = np.square(error)\n",
    "    reference = shifted_sigmoid(sqr_error, sigmoid_k, sigmoid_b)*(reference - error) + (1-shifted_sigmoid(sqr_error, sigmoid_k, sigmoid_b))*(R1)\n",
    "    # k = 0.1\n",
    "    # k =  zero_sigmoid(error)\n",
    "    # reference = reference - (error * k)\n",
    "    return reference\n",
    "\n",
    "# generate predictions on next state\n",
    "def imc(system_estimate, previous_state, behavioral_model, previous_output):\n",
    "    # actual estimate\n",
    "    state = (system_estimate * previous_state) + (behavioral_model * previous_output)\n",
    "    return state\n",
    "\n",
    "# adjust system estimate to infer observations given my behavior\n",
    "def imc_update(inputs, system_estimate, previous_behavior, behavioral_model, previous_state, learning_rate):\n",
    "    # actual estimate\n",
    "    #curr_system_est = (system_estimate @ previous_state) + (behavioral_model @ previous_behavior)\n",
    "    #error = curr_system_est - inputs\n",
    "    #learning_rate = 0.01\n",
    "    #system_estimate = system_estimate + learning_rate * error * previous_behavior\n",
    "    return system_estimate\n",
    "\n",
    "plant1 = Plant(transition_matrix=T1, transition_function=transition, state=behav_1)\n",
    "plant2 = Plant(transition_matrix=T2, transition_function=transition, state=behav_2)\n",
    "\n",
    "conformist1 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_true,\n",
    "controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "conformist2 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_true,\n",
    "controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "stubborn = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_false,\n",
    "controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B2, \n",
    "system_estimate=A2, reference=R2, init_behavior=behav_2)\n",
    "\n",
    "sigmoid = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_sigmoid,\n",
    "controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "system_estimate=A1, reference=R1, init_behavior=behav_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seed was: 21\n",
      "\n",
      " Sigmoid Model Agents (Error Relative to Expectation)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seed = random.randrange(1000)\n",
    "seed = 21\n",
    "np.random.seed(seed)\n",
    "print(\"Seed was:\", seed)\n",
    "w = 6; h = 5\n",
    "ncols = 3\n",
    "nrows = 1\n",
    "param_vals = [i for i in range(0, ncols*nrows)]\n",
    "tups = list(it.product(range(nrows), range(ncols)))\n",
    "agents_label = [\"Conformist\", \"Stubborn\", \"Mixed/Sigmoid\"]\n",
    "agents1 = [conformist2, stubborn, sigmoid]\n",
    "agents2 = [conformist1, conformist1, conformist1]\n",
    "fig, ax = plt.subplots(nrows, ncols, figsize=(w*ncols, h*nrows), dpi=100,\n",
    "                       sharey=True)#, sharex=True)\n",
    "plt.subplots_adjust(wspace=0.25,hspace=0.45)\n",
    "trials = 1000\n",
    "time = [i for i in range(trials)]\n",
    "\n",
    "color1 = [0.1,0.2,0.6, 0.7]\n",
    "color1c = [0.2,0.3,0.9, 0.4]\n",
    "color2 = [0.1,0.6,0.2, 0.7]\n",
    "color2c = [0.2,0.9,0.3, 0.4]\n",
    "color3 = [0.6,0.2,0.1, 0.7]\n",
    "color3c = [0.9,0.3,0.2, 0.4]\n",
    "colors = [color1,color2,color3]\n",
    "colors_c = [color1c,color2c,color3c]\n",
    "color_black = [0.1,0.1,0.1,0.5]\n",
    "\n",
    "for ix, param in enumerate(param_vals):\n",
    "    # experiments\n",
    "    conformist1 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_true,\n",
    "    controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "    system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "    conformist2 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_true,\n",
    "    controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "    system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "    stubborn = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_false,\n",
    "    controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B2, \n",
    "    system_estimate=A2, reference=R2, init_behavior=behav_2)\n",
    "\n",
    "    sigmoid = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_sigmoid,\n",
    "    controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "    system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "    plant1 = Plant(transition_matrix=T1, transition_function=transition, state=np.random.rand(1, behav_size))\n",
    "    plant2 = Plant(transition_matrix=T2, transition_function=transition, state=np.random.rand(1, behav_size))\n",
    "\n",
    "    b1 = np.random.rand(1, behav_size)\n",
    "    b2 = np.random.rand(1, behav_size)\n",
    "\n",
    "    agent1 = agents1[param%3]\n",
    "    agent2 = agents2[0]\n",
    "    agent1_error = []\n",
    "    agent2_error = []\n",
    "    sigmoid_value = []\n",
    "    for t in range(trials):\n",
    "        color1 = [0.1,0.2,0.8, (t+1)/(trials+1)]\n",
    "        color2 = [0.1,0.8,0.2, (t+1)/(trials+1)]\n",
    "        #pl.scatter(agent1.get_reference()[0],-0.03, c=color1, edgecolors=[0.8,0.1,0.1, color1[1]])\n",
    "        #pl.scatter(agent2.get_reference()[0],0, c=color2, edgecolors=[0.8,0.1,0.1, color2[3]], linewidth=1.5)\n",
    "        b1_control = agent1.go(b2)   \n",
    "        b2_control = agent2.go(b1)\n",
    "\n",
    "        # agent1_control.append(agent1.get_output())\n",
    "        # agent2_control.append(agent2.get_output())\n",
    "\n",
    "        b1 = plant1.transition(b1_control)\n",
    "        b2 = plant2.transition(b2_control)\n",
    "        \n",
    "        # agent1_behaviors.append(b1)\n",
    "        # agent2_behaviors.append(b2)\n",
    "\n",
    "        # agent1_error.append(np.absolute(agent1.get_error()))\n",
    "        # agent2_error.append(np.absolute(agent2.get_error()))\n",
    "        agent1_error.append(np.sum(np.square(agent1.get_error()[0]))/len(agent1.get_error()[0]))\n",
    "        agent2_error.append(np.sum(np.square(agent2.get_error()[0]))/len(agent2.get_error()[0]))\n",
    "        if agents_label[param] == \"Mixed/Sigmoid\": \n",
    "          sqr_error = np.square(agent2.get_error()[0])\n",
    "          sigmoid_value.append(shifted_sigmoid(sqr_error, sigmoid_k, sigmoid_b))\n",
    "\n",
    "        # agent1_reference.append(agent1.get_reference()[0])\n",
    "        # agent2_reference.append(agent2.get_reference()[0])\n",
    "\n",
    "    ai = tups[ix][1]\n",
    "    color = colors[param]\n",
    "    color_c = colors_c[param]\n",
    "    # plots\n",
    "    ax[ai].plot(time, agent1_error, color=color, label=agents_label[param])\n",
    "    ax[ai].plot(time, agent2_error, color=color_c, label=\"Conformist\")\n",
    "    if agents_label[param] == \"Mixed/Sigmoid\": \n",
    "      ax[ai].plot(time, sigmoid_value, color=color_black, label=\"Sigmoid Strategy\")\n",
    "    #   print(sigmoid_value)\n",
    "    if agents_label[param%3] == \"Conformist\":\n",
    "      ax[ai].set_title('Dual Conformists')\n",
    "    elif agents_label[param%3] == \"Stubborn\":\n",
    "      ax[ai].set_title('Stubborn Agent + Conformist')\n",
    "    else:\n",
    "      ax[ai].set_title('Mixed Strategy + Conformist')\n",
    "    ax[ai].legend()\n",
    "    # ax[ai].set_xlim(0,t)\n",
    "    ax[ai].set_ylim(0,1)\n",
    "    ax[ai].tick_params(axis='x')\n",
    "    ax[ai].tick_params(axis='y')\n",
    "    ax[ai].set_ylabel(\"Mean Square Error\")\n",
    "    ax[ai].set_xlabel(\"Time Step\")\n",
    "\n",
    "print(\"\\n Sigmoid Model Agents (Error Relative to Expectation)\")\n",
    "# print(\"Agent 1 base reference: {}\".format(R1))\n",
    "# print(\"Agent 2 base reference: {}\".format(R2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregate Simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seed was: 37\n",
      "Mean Conformist Expectation Error at final 10 timesteps:  2.8331687731089364e-32\n",
      "Mean Conformist Expectation Error at final 10 timesteps: 2.915993540830586e-32\n",
      "Mean Conformist Preference Error at final 10 timesteps:  0.17358174375367977\n",
      "Mean Conformist Preference Error at final 10 timesteps: 0.16721324691925088\n",
      "---\n",
      "Mean Conformist Expectation Error at final 10 timesteps:  0.0007651181421937628\n",
      "Mean Stubborn Expectation Error at final 10 timesteps: 0.18069428058842457\n",
      "Mean Conformist Preference Error at final 10 timesteps:  0.15830416181982893\n",
      "Mean Stubborn Preference Error at final 10 timesteps: 0.1804503969167913\n",
      "---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/m2/79c0250s6dv437ybqm3rcw1r0000gp/T/ipykernel_46250/2883308246.py:130: RuntimeWarning: divide by zero encountered in divide\n",
      "  ratio2.append(abs(d_behav2/agent1_raw_error_shifted))\n",
      "/var/folders/m2/79c0250s6dv437ybqm3rcw1r0000gp/T/ipykernel_46250/2883308246.py:130: RuntimeWarning: invalid value encountered in divide\n",
      "  ratio2.append(abs(d_behav2/agent1_raw_error_shifted))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Conformist Expectation Error at final 10 timesteps:  0.00038495476649781635\n",
      "Mean Mixed/Sigmoid Expectation Error at final 10 timesteps: 0.08682119103775979\n",
      "Mean Conformist Preference Error at final 10 timesteps:  0.18013639435404577\n",
      "Mean Mixed/Sigmoid Preference Error at final 10 timesteps: 0.17366975679578536\n",
      "---\n",
      "\n",
      " Sigmoid Model Agents (Error Relative to Expectation)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seed = random.randrange(1000)\n",
    "seed = 37\n",
    "np.random.seed(seed)\n",
    "print(\"Seed was:\", seed)\n",
    "w = 6; h = 5\n",
    "ncols = 3\n",
    "nrows = 1\n",
    "param_vals = [i for i in range(0, ncols*nrows)]\n",
    "tups = list(it.product(range(nrows), range(ncols)))\n",
    "agents_label = [\"Conformist\", \"Stubborn\", \"Mixed/Sigmoid\"]\n",
    "fig, ax = plt.subplots(nrows, ncols, figsize=(w*ncols, h*nrows), dpi=100,\n",
    "                       sharey=True)#, sharex=True)\n",
    "plt.subplots_adjust(wspace=0.25,hspace=0.45)\n",
    "experiments = 400\n",
    "trials = 1000\n",
    "time = [i for i in range(trials)]\n",
    "\n",
    "\n",
    "color1 = [0.1,0.2,0.6, 0.7]\n",
    "color1c = [0.2,0.3,0.9, 0.4]\n",
    "color2 = [0.1,0.6,0.2, 0.7]\n",
    "color2c = [0.2,0.9,0.3, 0.4]\n",
    "color3 = [0.6,0.2,0.1, 0.7]\n",
    "color3c = [0.9,0.3,0.2, 0.4]\n",
    "colors = [color1,color2,color3]\n",
    "colors_c = [color1c,color2c,color3c]\n",
    "color_black = [0.1,0.1,0.1,0.5]\n",
    "\n",
    "\n",
    "# the type of experiment\n",
    "for ix, param in enumerate(param_vals):\n",
    "    agent1_pref_errors = []\n",
    "    agent2_pref_errors = []\n",
    "    agent1_errors = []\n",
    "    agent2_errors = []\n",
    "    agent1_raw_errors = []\n",
    "    agent2_raw_errors = []\n",
    "    agent1_behaviorss = []\n",
    "    agent2_behaviorss = []\n",
    "    ratio1 = []\n",
    "    ratio2 = []\n",
    "    # the experiments\n",
    "    for e in range(experiments):\n",
    "      behav_1 = np.random.rand(1,behav_size)\n",
    "      behav_2 = np.random.rand(1,behav_size)\n",
    "      R1 = np.random.rand(1,behav_size)\n",
    "      R2 = np.random.rand(1,behav_size)\n",
    "      b1 = np.random.rand(1,behav_size)\n",
    "      b2 = np.random.rand(1,behav_size)\n",
    "      conformist1 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_true,\n",
    "      controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B2, \n",
    "      system_estimate=A2, reference=R2, init_behavior=behav_2)\n",
    "\n",
    "      conformist2 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_true,\n",
    "      controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "      system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "      stubborn = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_false,\n",
    "      controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "      system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "      sigmoid = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_sigmoid,\n",
    "      controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "      system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "      plant1 = Plant(transition_matrix=T1, transition_function=transition, state=np.random.rand(1, behav_size))\n",
    "      plant2 = Plant(transition_matrix=T2, transition_function=transition, state=np.random.rand(1, behav_size))\n",
    "      agents1 = [conformist2, stubborn, sigmoid]\n",
    "      agents2 = [conformist1, conformist1, conformist1]\n",
    "\n",
    "      # b1 = np.random.rand(1, 1)\n",
    "      # b2 = np.random.rand(1, 1)\n",
    "\n",
    "      agent1 = agents1[param]\n",
    "      agent2 = agents2[0]\n",
    "      agent1_error = []\n",
    "      agent2_error = []\n",
    "      agent1_pref_error = []\n",
    "      agent2_pref_error = []\n",
    "      agent1_raw_error = []\n",
    "      agent2_raw_error = []\n",
    "      sigmoid_value = []\n",
    "      agent1_behaviors = []\n",
    "      agent2_behaviors = []\n",
    "\n",
    "      # the time steps\n",
    "      for t in range(trials):\n",
    "          color1 = [0.1,0.2,0.8, (t+1)/(trials+1)]\n",
    "          color2 = [0.1,0.8,0.2, (t+1)/(trials+1)]\n",
    "\n",
    "          agent1_pref_error.append(np.sum(np.square(R1-b2))/behav_size)\n",
    "          agent2_pref_error.append(np.sum(np.square(R2-b1))/behav_size)\n",
    "\n",
    "          b1_control = agent1.go(b2)   \n",
    "          b2_control = agent2.go(b1)\n",
    "\n",
    "          agent1_behaviors.append(b1)\n",
    "          agent2_behaviors.append(b2)\n",
    "\n",
    "          agent1_raw_error.append(agent1.get_error()[0][0])\n",
    "          agent2_raw_error.append(agent2.get_error()[0][0])\n",
    "\n",
    "\n",
    "          b1 = plant1.transition(b1_control)\n",
    "          b2 = plant2.transition(b2_control)\n",
    "\n",
    "          agent1_error.append(np.sum(np.square(agent1.get_error())[0])/behav_size)\n",
    "          agent2_error.append(np.sum(np.square(agent2.get_error())[0])/behav_size)\n",
    "\n",
    "      agent1_errors.append(agent1_error)\n",
    "      agent2_errors.append(agent2_error)\n",
    "      agent1_pref_errors.append(agent1_pref_error)\n",
    "      agent2_pref_errors.append(agent2_pref_error)\n",
    "      agent1_raw_errors = []\n",
    "      agent2_raw_errors = []\n",
    "      agent1_behaviorss = []\n",
    "      agent1_behaviorss = []\n",
    "\n",
    "      dx = 1\n",
    "\n",
    "      b1_flat = [i[0][0]for i in agent1_behaviors]\n",
    "      b2_flat = [i[0][0]for i in agent2_behaviors]\n",
    "      d_behav1 = np.insert(np.diff(b1_flat, dx), 0, 0)\n",
    "      d_behav2 = np.insert(np.diff(b2_flat, dx), 0, 0)\n",
    "      agent1_raw_error.insert(-1, 1)\n",
    "      agent2_raw_error.insert(-1, 1)\n",
    "      agent1_raw_error_shifted = agent1_raw_error[1:]\n",
    "      agent2_raw_error_shifted = agent2_raw_error[1:]\n",
    "      ratio1.append(abs(d_behav1/agent2_raw_error_shifted))\n",
    "      ratio2.append(abs(d_behav2/agent1_raw_error_shifted))\n",
    "\n",
    "    error1_mat = np.array(agent1_errors)\n",
    "    error2_mat = np.array(agent2_errors)\n",
    "    pref_error1_mat = np.array(agent1_pref_errors)\n",
    "    pref_error2_mat = np.array(agent2_pref_errors)\n",
    "\n",
    "    mean_error1 = np.average(error1_mat, axis=0)\n",
    "    mean_error2 = np.average(error2_mat, axis=0)\n",
    "    mean_pref_error1 = np.average(pref_error1_mat, axis=0)\n",
    "    mean_pref_error2 = np.average(pref_error2_mat, axis=0)\n",
    "    std_error1  = error1_mat.std(0)\n",
    "    std_error2  = error2_mat.std(0)\n",
    "    std_pref_error1  = pref_error1_mat.std(0)\n",
    "    std_pref_error2  = pref_error2_mat.std(0)\n",
    "    t = [n for n in range(trials)]\n",
    "    ci1 = 2.96 * std_error1/np.sqrt(trials)\n",
    "    ci2 = 2.96 * std_error2/np.sqrt(trials)\n",
    "    pref_ci1 = 1.96 * std_pref_error1/np.sqrt(trials)\n",
    "    pref_ci2 = 1.96 * std_pref_error2/np.sqrt(trials)\n",
    "\n",
    "    upper1 = mean_error1+ci1\n",
    "    lower1 = mean_error1-ci1\n",
    "    upper1 = upper1.reshape(trials,)\n",
    "    lower1 = lower1.reshape(trials,)\n",
    "    upper2 = mean_error2+ci2\n",
    "    lower2 = mean_error2-ci2\n",
    "    upper2 = upper2.reshape(trials,)\n",
    "    lower2 = lower2.reshape(trials,)\n",
    "\n",
    "    pref_upper1 = mean_pref_error1+pref_ci1\n",
    "    pref_lower1 = mean_pref_error1-pref_ci1\n",
    "    pref_upper1 = pref_upper1.reshape(trials,)\n",
    "    pref_lower1 = pref_lower1.reshape(trials,)\n",
    "    pref_upper2 = mean_pref_error2+pref_ci2\n",
    "    pref_lower2 = mean_pref_error2-pref_ci2\n",
    "    pref_upper2 = pref_upper2.reshape(trials,)\n",
    "    pref_lower2 = pref_lower2.reshape(trials,)\n",
    "    \n",
    "    ai = tups[ix][1]\n",
    "    color = colors[param]\n",
    "    color_c = colors_c[param]\n",
    "    # plots\n",
    "    ax[ai].plot(time, mean_error1, color=color,label=agents_label[param])\n",
    "    ax[ai].plot(time, mean_error2, color=color_c, label=\"Conformist\")\n",
    "    ax[ai].fill_between(t, lower1, upper1, color=color, alpha=.1)\n",
    "    ax[ai].fill_between(t, lower2, upper2, color=color_c, alpha=.1)\n",
    "    # ax[ai].plot(time, agent1_error, color=color, label=agents_label[param])\n",
    "    # ax[ai].plot(time, agent2_error, color=color_c, label=\"Conformist\")\n",
    "    ax[ai].set_title('Agent 1: {}, Agent 2: Conformist'.format(agents_label[param]))\n",
    "    ax[ai].legend()\n",
    "    # ax[ai].set_xlim(0,t)\n",
    "    ax[ai].set_ylim(0,1)\n",
    "    ax[ai].tick_params(axis='x')\n",
    "    ax[ai].tick_params(axis='y')\n",
    "    ax[ai].set_ylabel(\"Mean Square Error\")\n",
    "    ax[ai].set_xlabel(\"Time Step\")\n",
    "    # ax[ai].tick_params(axis='y')\n",
    "    print(\"Mean Conformist Expectation Error at final 10 timesteps: \", np.mean(mean_error2[-10:]))\n",
    "    print(\"Mean {} Expectation Error at final 10 timesteps: {}\".format(agents_label[param], np.mean(mean_error1[-10:])))\n",
    "    print(\"Mean Conformist Preference Error at final 10 timesteps: \", np.mean(mean_pref_error2[-1]))\n",
    "    print(\"Mean {} Preference Error at final 10 timesteps: {}\".format(agents_label[param], np.mean(mean_pref_error1[-1])))\n",
    "    print(\"---\")\n",
    "\n",
    "print(\"\\n Sigmoid Model Agents (Error Relative to Expectation)\")\n",
    "# print(\"Agent 1 base reference: {}\".format(R1))\n",
    "# print(\"Agent 2 base reference: {}\".format(R2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Derivative Behavior / Error Correlation\n",
    "\n",
    "We are hoping to see high correlation of behavior change in one agent vs error in the other, particularly in times of drastic change. High correlation suggests the agent can use these periods to better infer the error and this parameters of the counterpart based on their future actions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "def matrix_deriv(mat, features, steps):\n",
    "  trans = mat.transpose()\n",
    "  deriv = np.empty([features, steps-1])\n",
    "  for feat in range(len(trans)):\n",
    "    deriv[feat] = np.diff(trans[feat], 1)\n",
    "  return deriv.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seed = random.randrange(1000)\n",
    "seed = 378\n",
    "np.random.seed(seed)\n",
    "\n",
    "behav_size = 50\n",
    "sigmoid_k = 200000000000\n",
    "sigmoid_b = 20\n",
    "# behavioral matrix (maps previous behavior to future behavior commands - how should I behave?)\n",
    "# behavioral matrix (maps previous behavior to future behavior commands - how should I behave?)\n",
    "B1 = np.random.rand(behav_size, behav_size)\n",
    "B2 = np.random.rand(behav_size, behav_size)\n",
    "# B1 = np.array([[1]])\n",
    "# B2 = np.array([[1]])\n",
    "\n",
    "# state/system matrix (maps previous state to future state - how will others behave (give past behaviors)?)\n",
    "A1 = np.random.rand(1, behav_size)\n",
    "A2 = np.random.rand(1, behav_size)\n",
    "\n",
    "# reference matrix (how do I expect/want others to behave? target values)\n",
    "R1 = np.random.rand(1, behav_size)\n",
    "R2 = np.random.rand(1, behav_size)\n",
    "\n",
    "# Initial behavior\n",
    "behav_1 = np.random.rand(1, behav_size)\n",
    "behav_2 = np.random.rand(1, behav_size)\n",
    "\n",
    "# transition matrix (maps control signal and previous state to current state - what behaviors do I enact given my intentions?)\n",
    "# T1 = np.array([.05])\n",
    "# T2 = np.array([.05])\n",
    "T1 = np.array([.1])\n",
    "T2 = np.array([.1])\n",
    "\n",
    "color2 = [0.1,0.6,0.2, 0.7]\n",
    "color2c = [0.2,0.9,0.3, 0.4]\n",
    "\n",
    "# get the state observation (behaviors of others)\n",
    "def sense(inputs):\n",
    "    perception = inputs\n",
    "    return perception\n",
    "\n",
    "# contrast reference with observations (and potentially incorporate predictions from internal model)\n",
    "def compare(sensory_signal, reference, prediction):\n",
    "    error = reference - sensory_signal\n",
    "    return error\n",
    "\n",
    "# generate a control signal\n",
    "def control(error, behavioral_model, previous_output):\n",
    "    control_signal = chaotic_controller(behavioral_model, 0.00001, error)\n",
    "    # print(error, control_signal)\n",
    "    # print(error/control_signal)\n",
    "    return control_signal\n",
    "\n",
    "# dictates how control commands determine state transitions - i.e. behaviors\n",
    "def transition(control_signal, transition_matrix, state):\n",
    "    new_state = (transition_matrix * control_signal) + state\n",
    "    new_state[new_state > 1] = 1\n",
    "    new_state[new_state < 0] = 0.0001\n",
    "    return new_state\n",
    "\n",
    "\n",
    "# adjusts behavioral model to get closer to behavior given \n",
    "def control_update(error, behavioral_model, previous_output):\n",
    "    # LMS algorithm\n",
    "    learning_rate = 0.0\n",
    "    #behavioral_model = behavioral_model + learning_rate * error * previous_output\n",
    "    return behavioral_model\n",
    "\n",
    "# adjusts reference given error\n",
    "def reference_update_true(reference, error):\n",
    "    k = 0.3\n",
    "    reference = reference - (error * k)\n",
    "    reference[reference > 1] = 1\n",
    "    reference[reference < 0] = 0.0001\n",
    "    return reference\n",
    "\n",
    "# does not adjust reference\n",
    "def reference_update_false(reference, error):\n",
    "    k = 0\n",
    "    reference = reference + (error * k)\n",
    "    return reference\n",
    "\n",
    "def reference_update_sigmoid(reference, error):\n",
    "    # reference = (matrix_sigmoid(error)-1)*(R1) + matrix_sigmoid(error)*(reference + error)\n",
    "    sqr_error = np.square(error)\n",
    "    reference = shifted_sigmoid(sqr_error, sigmoid_k, sigmoid_b)*(reference - error) + (1-shifted_sigmoid(sqr_error, sigmoid_k, sigmoid_b))*(R1)\n",
    "    # k = 0.1\n",
    "    # k =  zero_sigmoid(error)\n",
    "    # reference = reference - (error * k)\n",
    "    return reference\n",
    "\n",
    "# generate predictions on next state\n",
    "def imc(system_estimate, previous_state, behavioral_model, previous_output):\n",
    "    # actual estimate\n",
    "    state = (system_estimate * previous_state) + (behavioral_model * previous_output)\n",
    "    return state\n",
    "\n",
    "# adjust system estimate to infer observations given my behavior\n",
    "def imc_update(inputs, system_estimate, previous_behavior, behavioral_model, previous_state, learning_rate):\n",
    "    # actual estimate\n",
    "    #curr_system_est = (system_estimate @ previous_state) + (behavioral_model @ previous_behavior)\n",
    "    #error = curr_system_est - inputs\n",
    "    #learning_rate = 0.01\n",
    "    #system_estimate = system_estimate + learning_rate * error * previous_behavior\n",
    "    return system_estimate\n",
    "\n",
    "plant1 = Plant(transition_matrix=T1, transition_function=transition, state=behav_1)\n",
    "plant2 = Plant(transition_matrix=T2, transition_function=transition, state=behav_2)\n",
    "\n",
    "conformist1 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_true,\n",
    "controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "conformist2 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_true,\n",
    "controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "stubborn = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_false,\n",
    "controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B2, \n",
    "system_estimate=A2, reference=R2, init_behavior=behav_2)\n",
    "\n",
    "sigmoid = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_sigmoid,\n",
    "controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "system_estimate=A1, reference=R1, init_behavior=behav_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seed was: 88\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seed = random.randrange(1000)\n",
    "seed = 88\n",
    "# rng = random.Random(seed)\n",
    "print(\"Seed was:\", seed)\n",
    "np.random.seed(seed)\n",
    "w = 6; h = 5\n",
    "ncols = 3\n",
    "nrows = 1\n",
    "param_vals = [i for i in range(0, ncols*nrows)]\n",
    "tups = list(it.product(range(nrows), range(ncols)))\n",
    "agents_label = [\"Conformist\", \"Stubborn\", \"Mixed/Sigmoid\"]\n",
    "agents1 = [conformist2, stubborn, sigmoid]\n",
    "agents2 = [conformist1, conformist1, conformist1]\n",
    "fig, ax = plt.subplots(nrows*2, ncols, figsize=(w*ncols, h*nrows*3), dpi=100,\n",
    "                       sharey=True)#, sharex=True)\n",
    "plt.subplots_adjust(wspace=0.25,hspace=0.45)\n",
    "trials = 200\n",
    "time = [i for i in range(trials)]\n",
    "\n",
    "color1 = [0.1,0.2,0.6, 0.7]\n",
    "color1c = [0.2,0.3,0.9, 0.4]\n",
    "color2 = [0.1,0.6,0.2, 0.7]\n",
    "color2c = [0.2,0.9,0.3, 0.4]\n",
    "color3 = [0.6,0.2,0.1, 0.7]\n",
    "color3c = [0.9,0.3,0.2, 0.4]\n",
    "colors = [color1,color2,color3]\n",
    "colors_c = [color1c,color2c,color3c]\n",
    "color_black = [0.1,0.1,0.1,0.5]\n",
    "\n",
    "for ix, param in enumerate(param_vals):\n",
    "    # experiments\n",
    "    conformist1 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_true,\n",
    "    controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "    system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "    conformist2 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_true,\n",
    "    controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "    system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "    stubborn = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_false,\n",
    "    controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B2, \n",
    "    system_estimate=A2, reference=R2, init_behavior=behav_2)\n",
    "\n",
    "    sigmoid = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_sigmoid,\n",
    "    controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "    system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "    plant1 = Plant(transition_matrix=T1, transition_function=transition, state=np.random.rand(1, behav_size))\n",
    "    plant2 = Plant(transition_matrix=T2, transition_function=transition, state=np.random.rand(1, behav_size))\n",
    "\n",
    "    b1 = np.random.rand(1, behav_size)\n",
    "    b2 = np.random.rand(1, behav_size)\n",
    "\n",
    "    agent1 = agents1[param%3]\n",
    "    agent2 = agents2[0]\n",
    "    agent1_error = []\n",
    "    agent2_error = []\n",
    "    agent1_raw_error = []\n",
    "    agent2_raw_error = []\n",
    "    sigmoid_value = []\n",
    "    agent1_behaviors = []\n",
    "    agent2_behaviors = []\n",
    "    agent1_control = []\n",
    "    agent2_control = []\n",
    "    agent1_reference = []\n",
    "    agent2_reference = []\n",
    "    for t in range(trials):\n",
    "        color1 = [0.1,0.2,0.8, (t+1)/(trials+1)]\n",
    "        color2 = [0.1,0.8,0.2, (t+1)/(trials+1)]\n",
    "        #pl.scatter(agent1.get_reference()[0],-0.03, c=color1, edgecolors=[0.8,0.1,0.1, color1[1]])\n",
    "        #pl.scatter(agent2.get_reference()[0],0, c=color2, edgecolors=[0.8,0.1,0.1, color2[3]], linewidth=1.5)\n",
    "        b1_control = agent1.go(b2)   \n",
    "        b2_control = agent2.go(b1)\n",
    "\n",
    "        agent1_control.append(agent1.get_output())\n",
    "        agent2_control.append(agent2.get_output())\n",
    "\n",
    "        b1 = plant1.transition(b1_control)\n",
    "        b2 = plant2.transition(b2_control)\n",
    "        \n",
    "        agent1_behaviors.append(b1)\n",
    "        agent2_behaviors.append(b2)\n",
    "\n",
    "        # agent1_error.append(np.absolute(agent1.get_error()))\n",
    "        # agent2_error.append(np.absolute(agent2.get_error()))\n",
    "        agent1_raw_error.append(agent1.get_error()[0])\n",
    "        agent2_raw_error.append(agent2.get_error()[0])\n",
    "        # if b1[0][0] == 0.0 or b1[0][0] == 1.0:\n",
    "        #   agent1_raw_error.append(b1[0][0]+0.001)\n",
    "        # else:\n",
    "        #   agent1_raw_error.append(agent1.get_error()[0][0])\n",
    "        # if b2[0][0] == 0.0 or b2[0][0] == 1.0:\n",
    "        #    agent2_raw_error.append(b2[0][0]+0.001)\n",
    "        # else:\n",
    "        #   agent2_raw_error.append(agent2.get_error()[0][0])\n",
    "        \n",
    "        agent1_error.append(np.sum(np.square(agent1.get_error()[0]))/len(agent1.get_error()[0]))\n",
    "        agent2_error.append(np.sum(np.square(agent2.get_error()[0]))/len(agent2.get_error()[0]))\n",
    "        if agents_label[param] == \"Mixed/Sigmoid\": \n",
    "          sqr_error = np.square(agent2.get_error()[0])\n",
    "          sigmoid_value.append(shifted_sigmoid(sqr_error, sigmoid_k, sigmoid_b))\n",
    "\n",
    "        agent1_reference.append(agent1.get_reference()[0])\n",
    "        agent2_reference.append(agent2.get_reference()[0])\n",
    "\n",
    "    ai = tups[ix]\n",
    "    color = colors[param]\n",
    "    color_c = colors_c[param]\n",
    "    # plots\n",
    "    ax[ai].plot(time, agent1_error, color=color, label=agents_label[param])\n",
    "    ax[ai].plot(time, agent2_error, color=color_c, label=\"Conformist\")\n",
    "    # if agents_label[param] == \"Mixed/Sigmoid\": \n",
    "    #   ax[ai].plot(time, sigmoid_value, color=color_black, label=\"Sigmoid Strategy\")\n",
    "    #   print(sigmoid_value)\n",
    "    ax[ai].set_title('Agent 1 = {}, Agent 2 = Conformist'.format(agents_label[param%3]))\n",
    "    ax[ai].legend()\n",
    "    # ax[ai].set_xlim(0,t)\n",
    "    # ax[ai].set_ylim(0,1)\n",
    "    ax[ai].tick_params(axis='x')\n",
    "    ax[ai].tick_params(axis='y')\n",
    "    ax[ai].set_ylabel(\"Mean Square Error\")\n",
    "    # ax[ai].set_xlabel(\"Time Step\")\n",
    "\n",
    "    # derivative charts\n",
    "    dx = 1\n",
    "    # b1_flat = [i[0] for i in agent1_control]\n",
    "    # b2_flat = [i[0] for i in agent2_control]\n",
    "    b1_flat = [i[0]for i in agent1_behaviors]\n",
    "    # print(\"ag 1 behaviors: \", agent1_behaviors)\n",
    "    # print(\"ag 1 flat behaviors: \", b1_flat)\n",
    "    b2_flat = [i[0]for i in agent2_behaviors]\n",
    "    # d_behav1 = abs(np.gradient(b1_flat, dx))\n",
    "    # d_behav2 = abs(np.gradient(b2_flat, dx))\n",
    "    # d_behav1 = np.insert(np.diff(b1_flat, dx), 0, 0)\n",
    "    d_behav1 = matrix_deriv(np.array(b1_flat), behav_size, trials)[:-1]\n",
    "    # print(\"ag 1 behav deriv: \", d_behav1)\n",
    "    # d_behav2 = np.insert(np.diff(b2_flat, dx), 0, 0)\n",
    "    d_behav2 = matrix_deriv(np.array(b2_flat), behav_size, trials)[:-1]\n",
    "    # print(\"agent1_raw_error: \", agent1_raw_error)\n",
    "    # agent1_raw_error.insert(-1, 1)\n",
    "    # print(\"agent1_raw_error inserted: \", agent1_raw_error)\n",
    "    # agent2_raw_error.insert(-1, 1)\n",
    "    agent1_raw_error_shifted = agent1_raw_error[2:]\n",
    "    agent2_raw_error_shifted = agent2_raw_error[2:]\n",
    "\n",
    "    # print(agents_label[param%3], \"ratio: \", np.average(abs(d_behav1/agent2_raw_error_shifted)))\n",
    "    # print(agents_label[0], \"ratio: \", np.average(abs(d_behav2/agent1_raw_error_shifted)))\n",
    "    # print(agents_label[param%3], \"+ Conformist covariance:\", np.cov(np.absolute(d_behav1), np.absolute(agent2_raw_error_shifted)))\n",
    "    # print(agents_label[0], \"&\",agents_label[param%3], \"covariance:\", np.cov(np.absolute(d_behav2), np.absolute(agent1_raw_error_shifted)))\n",
    "    # print(agents_label[param%3], \"& Conformist Pearson R:\", np.corrcoef(np.absolute(d_behav1), np.absolute(agent2_raw_error_shifted))[0][1])\n",
    "    # print(agents_label[0], \"&\", agents_label[param%3], \" Pearson R:\", np.corrcoef(np.absolute(d_behav2), np.absolute(agent1_raw_error_shifted))[0][1])\n",
    "    # print(\"---\")\n",
    "\n",
    "    # print(agents_label[param])\n",
    "    # print(\"error: \", agent2_raw_error_shifted)\n",
    "    # print(\"d_behav: \", d_behav1)\n",
    "    coef1 = [np.corrcoef(d_behav1[i], agent2_raw_error_shifted[i])[0][1] for i in range(len(d_behav1))]\n",
    "    # print(coef1)\n",
    "    coef2 = [np.corrcoef(d_behav2[i], agent1_raw_error_shifted[i])[0][1] for i in range(len(d_behav2))]\n",
    "    \n",
    "    ax[(ai[0]+1,ai[1])].scatter([ i for i in range(trials-2)], coef1, color=color, label=\"{}\".format(agents_label[param]))\n",
    "    # ax[(ai[0]+1,ai[1])].scatter(np.absolute(d_behav1), np.absolute(agent2_raw_error_shifted), color=color, label=\"{}\".format(agents_label[param]))\n",
    "    ax[(ai[0]+1,ai[1])].scatter([ i for i in range(trials-2)], coef2, color=color_c, label=\"Conformist\")\n",
    "    # ax[(ai[0]+1,ai[1])].scatter(np.absolute(d_behav2), np.absolute(agent1_raw_error_shifted), color=color_c, label=\"Conformist\")\n",
    "\n",
    "    ax[(ai[0]+1,ai[1])].set_title('Agent 1 = {}, Agent 2 = Conformist'.format(agents_label[param%3]))\n",
    "    ax[(ai[0]+1,ai[1])].legend()\n",
    "    # ax[ai].set_ylim(0,1)\n",
    "    # ax[ai].set_xlim(0,t)\n",
    "    # ax[(ai[0]+1,ai[1])].set_ylim(0,5)\n",
    "    ax[(ai[0]+1,ai[1])].tick_params(axis='x')\n",
    "    ax[(ai[0]+1,ai[1])].tick_params(axis='y')\n",
    "    ax[(ai[0]+1,ai[1])].set_ylabel(\"Behavior Derivative / Error Correlation\")\n",
    "    ax[(ai[0]+1,ai[1])].set_xlabel(\"Time Step\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show That Sigmoid Agent Can Mimic Conformist "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seed was: 22\n"
     ]
    }
   ],
   "source": [
    "# seed = random.randrange(1000)\n",
    "seed = 22\n",
    "np.random.seed(seed)\n",
    "print(\"Seed was:\", seed)\n",
    "\n",
    "behav_size = 1\n",
    "sigmoid_k = 99999\n",
    "sigmoid_b = -5\n",
    "# behavioral matrix (maps previous behavior to future behavior commands - how should I behave?)\n",
    "# behavioral matrix (maps previous behavior to future behavior commands - how should I behave?)\n",
    "B1 = np.random.rand(1, behav_size)\n",
    "B2 = np.random.rand(1, behav_size)\n",
    "\n",
    "# state/system matrix (maps previous state to future state - how will others behave (give past behaviors)?)\n",
    "A1 = np.random.rand(1, behav_size)\n",
    "A2 = np.random.rand(1, behav_size)\n",
    "\n",
    "# reference matrix (how do I expect/want others to behave? target values)\n",
    "R1 = np.random.rand(1, behav_size)\n",
    "R2 = np.random.rand(1, behav_size)\n",
    "\n",
    "# Initial behavior\n",
    "behav_1 = np.random.rand(1, behav_size)\n",
    "behav_2 = np.random.rand(1, behav_size)\n",
    "\n",
    "# transition matrix (maps control signal and previous state to current state - what behaviors do I enact given my intentions?)\n",
    "T1 = np.array([0.2])\n",
    "T2 = np.array([0.2])\n",
    "\n",
    "color2 = [0.1,0.6,0.2, 0.7]\n",
    "color2c = [0.2,0.9,0.3, 0.4]\n",
    "\n",
    "# get the state observation (behaviors of others)\n",
    "def sense(inputs):\n",
    "    perception = inputs\n",
    "    return perception\n",
    "\n",
    "# contrast reference with observations (and potentially incorporate predictions from internal model)\n",
    "def compare(sensory_signal, reference, prediction):\n",
    "    error = reference - sensory_signal\n",
    "    return error\n",
    "\n",
    "# generate a control signal\n",
    "def control(error, behavioral_model, previous_output):\n",
    "    control_signal = chaotic_controller(behavioral_model, 0.01, error)\n",
    "    return control_signal\n",
    "\n",
    "# dictates how control commands determine state transitions - i.e. behaviors\n",
    "def transition(control_signal, transition_matrix, state):\n",
    "    new_state = (transition_matrix * control_signal) + state\n",
    "    new_state[new_state > 1] = 1\n",
    "    new_state[new_state < 0] = 0\n",
    "    return new_state\n",
    "\n",
    "\n",
    "# adjusts behavioral model to get closer to behavior given \n",
    "def control_update(error, behavioral_model, previous_output):\n",
    "    # LMS algorithm\n",
    "    learning_rate = 0.0\n",
    "    #behavioral_model = behavioral_model + learning_rate * error * previous_output\n",
    "    return behavioral_model\n",
    "\n",
    "# adjusts reference given error\n",
    "def reference_update_true(reference, error):\n",
    "    k = 0.2\n",
    "    reference = reference - (error * k)\n",
    "    reference[reference > 1] = 1\n",
    "    reference[reference < 0] = 0\n",
    "    return reference\n",
    "\n",
    "# does not adjust reference\n",
    "def reference_update_false(reference, error):\n",
    "    k = 0\n",
    "    reference = reference + (error * k)\n",
    "    return reference\n",
    "\n",
    "def reference_update_sigmoid(reference, error):\n",
    "    # reference = (matrix_sigmoid(error)-1)*(R1) + matrix_sigmoid(error)*(reference + error)\n",
    "    sqr_error = np.square(error)\n",
    "    reference = shifted_sigmoid(sqr_error, sigmoid_k, sigmoid_b)*(reference - error) + (1-shifted_sigmoid(sqr_error, sigmoid_k, sigmoid_b))*(R1)\n",
    "    # k = 0.1\n",
    "    # k =  zero_sigmoid(error)\n",
    "    # reference = reference - (error * k)\n",
    "    return reference\n",
    "\n",
    "# generate predictions on next state\n",
    "def imc(system_estimate, previous_state, behavioral_model, previous_output):\n",
    "    # actual estimate\n",
    "    state = (system_estimate * previous_state) + (behavioral_model * previous_output)\n",
    "    return state\n",
    "\n",
    "# adjust system estimate to infer observations given my behavior\n",
    "def imc_update(inputs, system_estimate, previous_behavior, behavioral_model, previous_state, learning_rate):\n",
    "    # actual estimate\n",
    "    #curr_system_est = (system_estimate @ previous_state) + (behavioral_model @ previous_behavior)\n",
    "    #error = curr_system_est - inputs\n",
    "    #learning_rate = 0.01\n",
    "    #system_estimate = system_estimate + learning_rate * error * previous_behavior\n",
    "    return system_estimate\n",
    "\n",
    "plant1 = Plant(transition_matrix=T1, transition_function=transition, state=behav_1)\n",
    "plant2 = Plant(transition_matrix=T2, transition_function=transition, state=behav_2)\n",
    "\n",
    "conformist1 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_true,\n",
    "controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "conformist2 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_true,\n",
    "controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "stubborn = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_false,\n",
    "controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B2, \n",
    "system_estimate=A2, reference=R2, init_behavior=behav_2)\n",
    "\n",
    "sigmoid = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_sigmoid,\n",
    "controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "system_estimate=A1, reference=R1, init_behavior=behav_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seed was: 159\n",
      "\n",
      " Sigmoid Model Agents (Error Relative to Expectation)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seed = random.randrange(1000)\n",
    "# seed = 14\n",
    "np.random.seed(seed)\n",
    "print(\"Seed was:\", seed)\n",
    "w = 6; h = 5\n",
    "ncols = 3\n",
    "nrows = 1\n",
    "param_vals = [i for i in range(0, ncols*nrows)]\n",
    "tups = list(it.product(range(nrows), range(ncols)))\n",
    "agents_label = [\"Conformist\", \"Stubborn\", \"Mixed/Sigmoid\"]\n",
    "agents1 = [conformist2, stubborn, sigmoid]\n",
    "agents2 = [conformist1, conformist1, conformist1]\n",
    "fig, ax = plt.subplots(nrows, ncols, figsize=(w*ncols, h*nrows), dpi=100,\n",
    "                       sharey=True)#, sharex=True)\n",
    "plt.subplots_adjust(wspace=0.25,hspace=0.45)\n",
    "trials = 1000\n",
    "time = [i for i in range(trials)]\n",
    "\n",
    "color1 = [0.1,0.2,0.6, 0.7]\n",
    "color1c = [0.2,0.3,0.9, 0.4]\n",
    "color2 = [0.1,0.6,0.2, 0.7]\n",
    "color2c = [0.2,0.9,0.3, 0.4]\n",
    "color3 = [0.6,0.2,0.1, 0.7]\n",
    "color3c = [0.9,0.3,0.2, 0.4]\n",
    "colors = [color1,color2,color3]\n",
    "colors_c = [color1c,color2c,color3c]\n",
    "color_black = [0.1,0.1,0.1,0.5]\n",
    "\n",
    "for ix, param in enumerate(param_vals):\n",
    "    # experiments\n",
    "    conformist1 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_true,\n",
    "    controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "    system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "    conformist2 = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_true,\n",
    "    controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "    system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "    stubborn = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_false,\n",
    "    controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B2, \n",
    "    system_estimate=A2, reference=R2, init_behavior=behav_2)\n",
    "\n",
    "    sigmoid = Control_node(sensor=sense, comparator=compare, control_update=control_update, reference_update=reference_update_sigmoid,\n",
    "    controller = control, internal_model=imc, internal_model_update=imc_update, behavioral_model=B1, \n",
    "    system_estimate=A1, reference=R1, init_behavior=behav_1)\n",
    "\n",
    "    plant1 = Plant(transition_matrix=T1, transition_function=transition, state=np.random.rand(1, behav_size))\n",
    "    plant2 = Plant(transition_matrix=T2, transition_function=transition, state=np.random.rand(1, behav_size))\n",
    "\n",
    "    b1 = np.random.rand(1, behav_size)\n",
    "    b2 = np.random.rand(1, behav_size)\n",
    "\n",
    "    agent1 = agents1[param%3]\n",
    "    agent2 = agents2[0]\n",
    "    agent1_error = []\n",
    "    agent2_error = []\n",
    "    sigmoid_value = []\n",
    "    for t in range(trials):\n",
    "        color1 = [0.1,0.2,0.8, (t+1)/(trials+1)]\n",
    "        color2 = [0.1,0.8,0.2, (t+1)/(trials+1)]\n",
    "        #pl.scatter(agent1.get_reference()[0],-0.03, c=color1, edgecolors=[0.8,0.1,0.1, color1[1]])\n",
    "        #pl.scatter(agent2.get_reference()[0],0, c=color2, edgecolors=[0.8,0.1,0.1, color2[3]], linewidth=1.5)\n",
    "        b1_control = agent1.go(b2)   \n",
    "        b2_control = agent2.go(b1)\n",
    "\n",
    "        # agent1_control.append(agent1.get_output())\n",
    "        # agent2_control.append(agent2.get_output())\n",
    "\n",
    "        b1 = plant1.transition(b1_control)\n",
    "        b2 = plant2.transition(b2_control)\n",
    "        \n",
    "        # agent1_behaviors.append(b1)\n",
    "        # agent2_behaviors.append(b2)\n",
    "\n",
    "        # agent1_error.append(np.absolute(agent1.get_error()))\n",
    "        # agent2_error.append(np.absolute(agent2.get_error()))\n",
    "        agent1_error.append(np.sum(np.square(agent1.get_error()[0]))/len(agent1.get_error()[0]))\n",
    "        agent2_error.append(np.sum(np.square(agent2.get_error()[0]))/len(agent2.get_error()[0]))\n",
    "        if agents_label[param] == \"Mixed/Sigmoid\": \n",
    "          sqr_error = np.square(agent2.get_error()[0])\n",
    "          sigmoid_value.append(shifted_sigmoid(sqr_error, sigmoid_k, sigmoid_b))\n",
    "\n",
    "        # agent1_reference.append(agent1.get_reference()[0])\n",
    "        # agent2_reference.append(agent2.get_reference()[0])\n",
    "\n",
    "    ai = tups[ix][1]\n",
    "    color = colors[param]\n",
    "    color_c = colors_c[param]\n",
    "    # plots\n",
    "    ax[ai].plot(time, agent1_error, color=color, label=agents_label[param])\n",
    "    ax[ai].plot(time, agent2_error, color=color_c, label=\"Conformist\")\n",
    "    if agents_label[param] == \"Mixed/Sigmoid\": \n",
    "      ax[ai].plot(time, sigmoid_value, color=color_black, label=\"Sigmoid Strategy\")\n",
    "    #   print(sigmoid_value)\n",
    "    if agents_label[param%3] == \"Conformist\":\n",
    "      ax[ai].set_title('Dual Conformists')\n",
    "    elif agents_label[param%3] == \"Stubborn\":\n",
    "      ax[ai].set_title('Stubborn Agent + Conformist')\n",
    "    else:\n",
    "      ax[ai].set_title('Mixed Strategy + Conformist')\n",
    "    ax[ai].legend()\n",
    "    # ax[ai].set_xlim(0,t)\n",
    "    ax[ai].set_ylim(0,1)\n",
    "    ax[ai].tick_params(axis='x')\n",
    "    ax[ai].tick_params(axis='y')\n",
    "    ax[ai].set_ylabel(\"Mean Square Error\")\n",
    "    ax[ai].set_xlabel(\"Time Step\")\n",
    "\n",
    "print(\"\\n Sigmoid Model Agents (Error Relative to Expectation)\")\n",
    "# print(\"Agent 1 base reference: {}\".format(R1))\n",
    "# print(\"Agent 2 base reference: {}\".format(R2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scratch Work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.0\n",
      "[ 0.01454089 -0.03218108 -0.02313377 -0.00850707 -0.01922031 -0.01679566\n",
      "  0.01273211 -0.00429548  0.01957796 -0.03047746 -0.02216484 -0.03975788\n",
      "  0.00681055 -0.01449988 -0.01151191 -0.00864103 -0.02958216  0.00180413\n",
      " -0.00221501 -0.02970185  0.00749252  0.01372076  0.01500398 -0.00993083\n",
      " -0.02931745 -0.02013294 -0.01334102 -0.01371742 -0.0117961  -0.00565544\n",
      "  0.00504841 -0.01573635 -0.01987115 -0.0191835  -0.03256478 -0.00899477\n",
      " -0.01350633 -0.01004508 -0.02473687  0.00992636 -0.01566465 -0.0193648\n",
      " -0.0243776   0.00662096 -0.02030571 -0.02337517 -0.01222031 -0.02276325\n",
      " -0.01309425  0.03063215]\n",
      "[-0.01454089  0.03218108  0.02313377  0.00850707  0.01922031  0.01679566\n",
      " -0.01273211  0.00429548 -0.01957796  0.03047746  0.02216484  0.03975788\n",
      " -0.00681055  0.01449988  0.01151191  0.00864103  0.02958216 -0.00180413\n",
      "  0.00221501  0.02970185 -0.00749252 -0.01372076 -0.01500398  0.00993083\n",
      "  0.02931745  0.02013294  0.01334102  0.01371742  0.0117961   0.00565544\n",
      " -0.00504841  0.01573635  0.01987115  0.0191835   0.03256478  0.00899477\n",
      "  0.01350633  0.01004508  0.02473687 -0.00992636  0.01566465  0.0193648\n",
      "  0.0243776  -0.00662096  0.02030571  0.02337517  0.01222031  0.02276325\n",
      "  0.01309425 -0.03063215]\n"
     ]
    }
   ],
   "source": [
    "# investigating -1 conformist correlations in sigmoid case\n",
    "print(coef2[54])\n",
    "print(agent1_raw_error_shifted[54])\n",
    "print(d_behav2[54])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.9991014435608242, nan, -0.959678749500367]\n",
      "[-0.03770918 -0.05964366 -0.05894456 -0.00916881 -0.00847428  0.0004833\n",
      " -0.01713752 -0.01971938 -0.11384336 -0.0017065  -0.09880912 -0.03233566\n",
      " -0.06434915 -0.06303439 -0.00203614 -0.00427229 -0.01819278 -0.00958058\n",
      " -0.0147159   0.00325058 -0.02329864 -0.03354375 -0.03235218 -0.07154833\n",
      " -0.08456604 -0.07224736 -0.00815107 -0.0904591  -0.08294256 -0.02042623\n",
      " -0.04912403 -0.05699172 -0.02419513 -0.0166227  -0.08161547 -0.06186383\n",
      " -0.04022385 -0.05988196 -0.10868714 -0.01317207 -0.09369284 -0.00925103\n",
      " -0.06266531 -0.06732191 -0.06723726 -0.02016818 -0.09947293 -0.00696132\n",
      " -0.10028535 -0.09641529]\n",
      "[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. 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.]\n"
     ]
    }
   ],
   "source": [
    "# investigating -1 conformist correlations in sigmoid case\n",
    "print(coef1[84:87])\n",
    "print(agent2_raw_error_shifted[85])\n",
    "print(d_behav1[85])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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