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@ccrock4t
ccrock4t committed Jan 22, 2022
1 parent 88ba7d0 commit dce8c50
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Showing 6 changed files with 136 additions and 126 deletions.
10 changes: 5 additions & 5 deletions Config.json
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Original file line number Diff line number Diff line change
@@ -1,8 +1,8 @@
{
"k": 1,
"T": 0.65,
"FOCUSX": 1.0305365040669583,
"FOCUSY": 0.8437127611721296,
"T": 0.6,
"FOCUSX": 1.2305365040669583,
"FOCUSY": 1.0437127611721296,
"PROJECTION_DECAY_DESIRE": 0.95,
"PROJECTION_DECAY_EVENT": 0.95,

Expand All @@ -16,8 +16,8 @@
"POSITIVE_THRESHOLD": 0.51,
"NEGATIVE_THRESHOLD": 0.5,

"MEMORY_CONCEPT_CAPACITY": 210000,
"EVENT_BUFFER_CAPACITY": 10,
"MEMORY_CONCEPT_CAPACITY": 300000,
"EVENT_BUFFER_CAPACITY": 15,
"GLOBAL_BUFFER_CAPACITY": 1000,
"CONCEPT_LINK_CAPACITY": 100,
"NUMBER_OF_ATTEMPTS_TO_SEARCH_FOR_SEMANTICALLY_RELATED_CONCEPT": 3,
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Binary file modified Documentation/Diagram.png
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44 changes: 22 additions & 22 deletions NALInferenceRules/HelperFunctions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -161,28 +161,28 @@ def stamp_and_print_inference_rule(sentence, inference_rule, parent_sentences):
for parent in parent_sentences:
sentence.stamp.parent_premises.append(parent)

if isinstance(parent.statement, NALGrammar.Terms.SpatialTerm):
parent_strings.append("CENTER: " + str(parent.statement.center) + " | DIM:"
+ str(parent.statement.dimensions) + " " + str(parent.value)
+ " -- pooled? " + str(parent.statement.of_spatial_terms))
elif isinstance(parent.statement, NALGrammar.Terms.CompoundTerm) and not isinstance(parent, NALGrammar.Sentences.Goal):
parent_strings.append("CENTER1: " + str(parent.statement.subterms[0].center) + " | DIM:"
+ str(parent.statement.subterms[0].dimensions)
+ " -- pooled? " + str(parent.statement.subterms[0].of_spatial_terms)
+ " " + str(parent.value))

parent_strings.append("CENTER2: " + str(parent.statement.subterms[1].center) + " | DIM:"
+ str(parent.statement.subterms[1].dimensions) + " " + str(parent.value)
+ " -- pooled? " + str(parent.statement.subterms[1].of_spatial_terms))

else:
parent_strings.append("other " + str(parent.value))


# if inference_rule is F_Deduction and isinstance(sentence, NALGrammar.Sentences.Judgment) and sentence.statement.is_first_order():
# Global.Global.debug_print(sentence.stamp.derived_by
# + " derived " + sentence.get_formatted_string()
# + " by parents " + str(parent_strings))
# if isinstance(parent.statement, NALGrammar.Terms.SpatialTerm):
# parent_strings.append("CENTER: " + str(parent.statement.center) + " | DIM:"
# + str(parent.statement.dimensions) + " " + str(parent.value)
# + " -- pooled? " + str(parent.statement.of_spatial_terms))
# elif isinstance(parent.statement, NALGrammar.Terms.CompoundTerm) and not isinstance(parent, NALGrammar.Sentences.Goal):
# parent_strings.append("CENTER1: " + str(parent.statement.subterms[0].center) + " | DIM:"
# + str(parent.statement.subterms[0].dimensions)
# + " -- pooled? " + str(parent.statement.subterms[0].of_spatial_terms)
# + " " + str(parent.value))
#
# parent_strings.append("CENTER2: " + str(parent.statement.subterms[1].center) + " | DIM:"
# + str(parent.statement.subterms[1].dimensions) + " " + str(parent.value)
# + " -- pooled? " + str(parent.statement.subterms[1].of_spatial_terms))
#
# else:
# parent_strings.append("other " + str(parent.value))


if inference_rule is F_Deduction and isinstance(sentence, NALGrammar.Sentences.Judgment) and sentence.statement.is_first_order():
Global.Global.debug_print(sentence.stamp.derived_by
+ " derived " + sentence.get_formatted_string()
+ " by parents " + str(parent_strings))

def premise_result_type(j1,j2):
"""
Expand Down
22 changes: 10 additions & 12 deletions NARS.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,8 +68,8 @@ def __init__(self):

Global.Global.NARS = self # global vars are part of NARS
Global.Global.ARRAY_NEGATIVE_ELEMENT = NALGrammar.Terms.from_string('(--,(arrayEl-->negative))')
Global.Global.ARRAY_NEGATIVE_SENTENCE = NALGrammar.Sentences.Judgment(statement=Global.Global.ARRAY_NEGATIVE_ELEMENT.subterms[0],
value=NALGrammar.Values.TruthValue(frequency=0.0))
Global.Global.ARRAY_NEGATIVE_SENTENCE = NALGrammar.Sentences.Judgment(statement=Global.Global.ARRAY_NEGATIVE_ELEMENT,
value=NALGrammar.Values.TruthValue(frequency=1.0))


def startup_and_run(self):
Expand Down Expand Up @@ -120,16 +120,14 @@ def do_working_cycle(self):
# OBSERVE
#self.Observe()
# todo begin spatial take vvv
radii = [1]
vision_sentences = []
for radius in radii:
vision_sentences += self.vision_buffer.take(pool=False,radius=radius)
vision_sentences += self.vision_buffer.take(pool=True,radius=radius)

# single array
for vision_sentence in vision_sentences:
if vision_sentence is not None:
self.global_buffer.PUT_NEW(NARSDataStructures.Other.Task(vision_sentence))
vision_sentence = self.vision_buffer.take(pooled=False)
if vision_sentence is not None:
self.global_buffer.PUT_NEW(NARSDataStructures.Other.Task(vision_sentence))

vision_sentence = self.vision_buffer.take(pooled=True)
if vision_sentence is not None:
self.global_buffer.PUT_NEW(NARSDataStructures.Other.Task(vision_sentence))

# todo end spatial take ^^

Expand Down Expand Up @@ -399,7 +397,7 @@ def process_judgment_task(self, task: NARSDataStructures.Other.Task):
# only put non-derived atomic events in temporal module for now
Global.Global.NARS.temporal_module.PUT_NEW(task)

if isinstance(task.sentence.statement, NALGrammar.Terms.SpatialTerm): return
if task.sentence.stamp.derived_by is None: return

if isinstance(j.statement, NALGrammar.Terms.CompoundTerm)\
and j.statement.connector == NALSyntax.TermConnector.Negation:
Expand Down
176 changes: 94 additions & 82 deletions NARSDataStructures/Buffers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -87,19 +87,20 @@ def __init__(self, dimensions):
self.dimensions = dimensions
self.array = np.full(shape=dimensions,
fill_value=NALGrammar.Sentences.TruthValue(0.0,0.9))
self.pooled_array = None
self.components_bag = Bag(item_type=tuple,
self.components_bag = Bag(item_type=object,
capacity=1000,
granularity=100)
self.pooled_components_bag = Bag(item_type=tuple,

self.pooled_array = np.full(shape=dimensions,
fill_value=NALGrammar.Sentences.TruthValue(0.0,0.9))
self.pooled_components_bag = Bag(item_type=object,
capacity=1000,
granularity=100)

self.last_taken_img_array = None
self.last_sentence = None

# initialize with uniform probabilility
for indices, truth_value in np.ndenumerate(self.array):
item = self.components_bag.PUT_NEW(indices)
self.components_bag.change_priority(item.key, 0.5)

def blank_image(self):
self.set_image(np.empty(shape=self.array.shape))
Expand All @@ -112,102 +113,105 @@ def set_image(self, img):
# "ERROR: Data dimensions are incompatible with Spatial Buffer dimensions " \
# + str(event_array.shape) + " and " + str(self.dimensions)

self.array = self.create_pooled_sensation_array(original_event_array, stride=2)
self.array = self.create_pooled_sensation_array(original_event_array, stride=1)
self.components_bag.clear()
self.pooled_components_bag.clear()

# maximum = 0
# for indices, sentence in np.ndenumerate(self.array):
# e = NALInferenceRules.TruthValueFunctions.Expectation(sentence.value.frequency, sentence.value.confidence)
# maximum = max(maximum, 2 * abs(e - 0.5))
maximum = 0
for indices, sentence in np.ndenumerate(self.array):
if sentence.value.frequency > Config.POSITIVE_THRESHOLD \
and not (isinstance(sentence.statement,
NALGrammar.Terms.CompoundTerm) and sentence.statement.connector == NALSyntax.TermConnector.Negation):
maximum = max(maximum, sentence.value.frequency * sentence.value.confidence)

for indices, sentence in np.ndenumerate(self.array):
#e = NALInferenceRules.TruthValueFunctions.Expectation(sentence.value.frequency,sentence.value.confidence)
priority = sentence.value.frequency #2 * abs(e - 0.5) / maximum # normalized priority
self.components_bag.PUT_NEW(indices)
self.components_bag.change_priority(Item.get_key_from_object(indices), priority)
if sentence.value.frequency > Config.POSITIVE_THRESHOLD \
and not (isinstance(sentence.statement,NALGrammar.Terms.CompoundTerm) and sentence.statement.connector == NALSyntax.TermConnector.Negation):
priority = sentence.value.frequency * sentence.value.confidence / maximum
object = indices
self.components_bag.PUT_NEW(object)
self.components_bag.change_priority(Item.get_key_from_object(object), priority)


# now the pooled array
self.pooled_array = self.create_pooled_sensation_array(original_event_array, stride=1)
self.pooled_array = self.create_pooled_sensation_array(self.pooled_array, stride=2)
# maximum = 0
# for indices, sentence in np.ndenumerate(self.pooled_array):
# e = NALInferenceRules.TruthValueFunctions.Expectation(sentence.value.frequency, sentence.value.confidence)
# maximum = max(maximum, 2 * abs(e - 0.5))

# pooled
self.pooled_array = self.create_pooled_sensation_array(original_event_array, stride=2)
#self.pooled_array = self.create_pooled_sensation_array(self.pooled_array , stride=2)
self.pooled_components_bag.clear()

maximum = 0
for indices, sentence in np.ndenumerate(self.pooled_array):
if sentence.value.frequency > Config.POSITIVE_THRESHOLD \
and not (isinstance(sentence.statement,
NALGrammar.Terms.CompoundTerm) and sentence.statement.connector == NALSyntax.TermConnector.Negation):
maximum = max(maximum, sentence.value.frequency * sentence.value.confidence)

for indices, sentence in np.ndenumerate(self.pooled_array):
#e = NALInferenceRules.TruthValueFunctions.Expectation(sentence.value.frequency,sentence.value.confidence)
priority = sentence.value.frequency #2 * abs(e - 0.5) / maximum # normalized priority
self.pooled_components_bag.PUT_NEW(indices)
self.pooled_components_bag.change_priority(Item.get_key_from_object(indices), priority)

# maximum = 0
# for indices, sentence in np.ndenumerate(self.array):
#
#
# for indices, sentence in np.ndenumerate(self.array):
#
# self.components_bag.PUT_NEW(indices)
# self.components_bag.change_priority(Item.get_key_from_object(indices), priority)

def take(self, pool, radius):
if sentence.value.frequency > Config.POSITIVE_THRESHOLD \
and not (isinstance(sentence.statement,
NALGrammar.Terms.CompoundTerm) and sentence.statement.connector == NALSyntax.TermConnector.Negation):
priority = sentence.value.frequency * sentence.value.confidence / maximum
object = indices
self.pooled_components_bag.PUT_NEW(object)
self.pooled_components_bag.change_priority(Item.get_key_from_object(object), priority)

def take(self, pooled):
"""
Probabilistically select a spatial subset of the buffer.
:return: an Array Judgment of the selected subset.
"""

if pool:
if pooled:
bag = self.pooled_components_bag
array = self.pooled_array
else:
bag = self.components_bag

array = self.array

# probabilistically peek the 2 vertices of the box
# selection 1: small fixed windows

indices = bag.peek()
if indices is None: return None

y, x = indices.object
radius = 1#random.randint(1,2)
min_x, min_y = max(x - radius, 0), max(y - radius, 0)
max_x, max_y = min(x + radius, array.shape[1] - 1), min(y + radius,
array.shape[0] - 1)

extracted = array[min_y:max_y+1, min_x:max_x+1]
sentence_subset= []
for idx,sentence in np.ndenumerate(extracted):
if not (isinstance(sentence.statement, NALGrammar.Terms.CompoundTerm)
and sentence.statement.connector == NALSyntax.TermConnector.Negation):
sentence_subset.append(sentence)

total_truth = None
statement_subset = []
for sentence in sentence_subset:
if total_truth is None:
total_truth = sentence.value
else:
total_truth = NALInferenceRules.TruthValueFunctions.F_Intersection(sentence.value.frequency,
sentence.value.confidence,
total_truth.frequency,
total_truth.confidence)
statement_subset.append(sentence.statement)

# selection 1: small fixed windows
item = bag.peek()
radius = radius#random.randint(1,3)
element_idx_y, element_idx_x = item.object

min_x, min_y = max(element_idx_x - radius, 0), max(element_idx_y - radius, 0)
max_x, max_y = min(element_idx_x + radius, self.array.shape[1]-1), min(element_idx_y + radius, self.array.shape[0]-1)


# selection 2: bounding box from random features
# item1 = bag.peek()
# item2 = item1
# while item2 is item1:
# item2 = bag.peek()
#
# element1_idx_y, element1_idx_x = item1.object
# element2_idx_y, element2_idx_x = item2.object
#
# min_x, min_y = min(element1_idx_x, element2_idx_x), min(element1_idx_y, element2_idx_y)
# max_x, max_y = max(element1_idx_x, element2_idx_x), max(element1_idx_y, element2_idx_y)

# extract subset of elements inside the bounding box (inclusive)
if pool:
event_subset = self.pooled_array[min_y:max_y + 1, min_x:max_x + 1]
else:
event_subset = self.array[min_y:max_y+1, min_x:max_x+1]

judgment = self.create_spatial_conjunction(event_subset)
# create conjunction of features
statement = NALGrammar.Terms.CompoundTerm(subterms=statement_subset,
term_connector=NALSyntax.TermConnector.Conjunction)

judgments = [judgment]
event_sentence = NALGrammar.Sentences.Judgment(statement=statement,
value=total_truth,
occurrence_time=Global.Global.get_current_cycle_number())

center = ((min_y, min_x), (max_y, max_x))
for j in judgments:
if j is not None:
j.statement.center = center

if not pool:
last_taken_img_array = np.zeros(shape=self.img.shape)
last_taken_img_array[min_y*2:(max_y+1)*2, 2*min_x:2*(max_x+1)] = self.img[min_y*2:(max_y+1)*2, 2*min_x:2*(max_x+1)]
self.last_taken_img_array = last_taken_img_array # store for visualization
# last_taken_img_array = np.zeros(shape=self.img.shape)
# last_taken_img_array[min_y+1:(max_y+1)+1, min_x+1:(max_x+1)+1] = self.img[min_y+1:(max_y+1)+1, min_x+1:(max_x+1)+1]
# self.last_taken_img_array = last_taken_img_array # store for visualization

return judgments
return event_sentence

def create_spatial_conjunction(self, subset):
"""
Expand Down Expand Up @@ -486,7 +490,10 @@ def process_sentence(derived_sentence):
event_task_A = temporal_chain[i].object
event_A = event_task_A.sentence

if not (isinstance(event_A.statement, NALGrammar.Terms.CompoundTerm) and NALSyntax.TermConnector.is_conjunction(event_A.statement.connector)): continue
if not (isinstance(event_A.statement, NALGrammar.Terms.CompoundTerm)
and NALSyntax.TermConnector.is_conjunction(event_A.statement.connector)
and isinstance(event_A.statement.subterms[0], NALGrammar.Terms.CompoundTerm)
and NALSyntax.TermConnector.is_conjunction(event_A.statement.subterms[0].connector)): continue

derived_sentences = NARSInferenceEngine.do_temporal_inference_two_premise(event_A, event_B)

Expand All @@ -499,7 +506,7 @@ def temporal_chaining_2_conjunction(self):
Perform temporal chaining
produce all possible forward implication statements using temporal induction and intersection
A =/> B
A && B
for the latest statement in the chain
"""
Expand All @@ -510,7 +517,9 @@ def temporal_chaining_2_conjunction(self):
event_task_B = self.get_most_recent_event_task().object
event_B = event_task_B.sentence

if not isinstance(event_B.statement, NALGrammar.Terms.SpatialTerm): return
if not (isinstance(event_B.statement, NALGrammar.Terms.CompoundTerm)
and NALSyntax.TermConnector.is_conjunction(event_B.statement.connector)
and (isinstance(event_B.statement.subterms[0], NALGrammar.Terms.SpatialTerm) or isinstance(event_B.statement.subterms[0], NALGrammar.Terms.StatementTerm))): return

def process_sentence(derived_sentence):
if derived_sentence is not None:
Expand All @@ -523,7 +532,10 @@ def process_sentence(derived_sentence):
event_task_A = temporal_chain[i].object
event_A = event_task_A.sentence

if not isinstance(event_A.statement, NALGrammar.Terms.SpatialTerm): continue
if not (isinstance(event_A.statement, NALGrammar.Terms.CompoundTerm)
and NALSyntax.TermConnector.is_conjunction(event_A.statement.connector)
and (isinstance(event_A.statement.subterms[0], NALGrammar.Terms.SpatialTerm) or isinstance(
event_A.statement.subterms[0], NALGrammar.Terms.StatementTerm))): return

derived_sentences = NARSInferenceEngine.do_temporal_inference_two_premise(event_A, event_B)

Expand Down
10 changes: 5 additions & 5 deletions TestCases/MNISTVisionTests.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -216,11 +216,11 @@ def binary_classification():

def digit_classification():
restart_NARS()
length = 400
training_cycles = 125
length = 500
training_cycles = 200
x_train, y_train, x_test, y_test = load_dataset(length=length,
bit=False,
percent_of_train_img=0.75)
percent_of_train_img=0.8)

"""
Training Phase
Expand Down Expand Up @@ -719,7 +719,7 @@ def run_trials(q, current_best_score):
q.put(avg_score)

def test_main():
time.sleep(10)
time.sleep(1)
learn_best_params()
#supervised_learning_binary_one_example()

Expand All @@ -739,7 +739,7 @@ def test_main():

if __name__ == "__main__":
global_gui = MNISTVisionTestGUI()
global_gui.gui_disabled = False
global_gui.gui_disabled = True
if global_gui.gui_disabled:
global_gui.start()
else:
Expand Down

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