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test_memory.py
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test_memory.py
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from itertools import combinations
import numpy as np
import pytest
import psyneulink.core.components.functions.stateful.memoryfunctions as Functions
import psyneulink.core.llvm as pnlvm
from psyneulink import *
from psyneulink.core.globals.utilities import _SeededPhilox
# **********************************************************************************************************************
# OMINBUS TEST *********************************************************************************************************
# **********************************************************************************************************************
#region
module_seed = 0
np.random.seed(0)
SIZE=10
test_var = np.random.rand(2, SIZE)
test_initializer = np.array([[test_var[0] * 5, test_var[1] * 4]])
test_noise_arr = np.random.rand(SIZE)
RAND1 = np.random.random(1)
RAND2 = np.random.random()
philox_var = np.random.rand(2, SIZE)
test_data = [
# Default initializer does not work
# (Functions.Buffer, test_var, {'rate':RAND1}, [[0.0],[0.0]]),
pytest.param(Functions.Buffer, test_var[0], {'history':512, 'rate':RAND1, 'initializer':[test_var[0]]},
[[0.03841128, 0.05005587, 0.04218721, 0.0381362 , 0.02965146, 0.04520592, 0.03062659, 0.0624149 , 0.06744644, 0.02683695],
[0.14519169, 0.18920736, 0.15946443, 0.1441519 , 0.11208025, 0.17087491, 0.11576615, 0.23592355, 0.25494239, 0.10144161]], id="Buffer"),
pytest.param(Functions.DictionaryMemory, test_var, {'seed': module_seed},
[[0.5488135039273248, 0.7151893663724195, 0.6027633760716439, 0.5448831829968969, 0.4236547993389047, 0.6458941130666561, 0.4375872112626925, 0.8917730007820798, 0.9636627605010293, 0.3834415188257777],
[0.7917250380826646, 0.5288949197529045, 0.5680445610939323, 0.925596638292661, 0.07103605819788694, 0.08712929970154071, 0.02021839744032572, 0.832619845547938, 0.7781567509498505, 0.8700121482468192 ]],
id="DictionaryMemory"),
pytest.param(Functions.DictionaryMemory, test_var, {'rate':RAND1, 'seed': module_seed},
[[0.5488135039273248, 0.7151893663724195, 0.6027633760716439, 0.5448831829968969, 0.4236547993389047, 0.6458941130666561, 0.4375872112626925, 0.8917730007820798, 0.9636627605010293, 0.3834415188257777],
[0.7917250380826646, 0.5288949197529045, 0.5680445610939323, 0.925596638292661, 0.07103605819788694, 0.08712929970154071, 0.02021839744032572, 0.832619845547938, 0.7781567509498505, 0.8700121482468192]],
id="DictionaryMemory Rate"),
pytest.param(Functions.DictionaryMemory, test_var, {'initializer':test_initializer, 'rate':RAND1, 'seed': module_seed},
[[0.5488135039273248, 0.7151893663724195, 0.6027633760716439, 0.5448831829968969, 0.4236547993389047, 0.6458941130666561, 0.4375872112626925, 0.8917730007820798, 0.9636627605010293, 0.3834415188257777],
[0.7917250380826646, 0.5288949197529045, 0.5680445610939323, 0.925596638292661, 0.07103605819788694, 0.08712929970154071, 0.02021839744032572, 0.832619845547938, 0.7781567509498505, 0.8700121482468192]],
id="DictionaryMemory Initializer"),
pytest.param(Functions.DictionaryMemory, test_var, {'rate':RAND1, 'retrieval_prob':0.5, 'seed': module_seed},
[[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]],
id="DictionaryMemory Low Retrieval"),
pytest.param(Functions.DictionaryMemory, test_var, {'rate':RAND1, 'storage_prob':0.1, 'seed': module_seed},
[[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]],
id="DictionaryMemory Low Storage"),
pytest.param(Functions.DictionaryMemory, test_var, {'rate':RAND1, 'retrieval_prob':0.9, 'storage_prob':0.9, 'seed': module_seed},
[[0.5488135039273248, 0.7151893663724195, 0.6027633760716439, 0.5448831829968969, 0.4236547993389047, 0.6458941130666561, 0.4375872112626925, 0.8917730007820798, 0.9636627605010293, 0.3834415188257777],
[0.7917250380826646, 0.5288949197529045, 0.5680445610939323, 0.925596638292661, 0.07103605819788694, 0.08712929970154071, 0.02021839744032572, 0.832619845547938, 0.7781567509498505, 0.8700121482468192]],
id="DictionaryMemory High Storage/Retrieve"),
# Disable noise tests for now as they trigger failure in DictionaryMemory lookup
# (Functions.DictionaryMemory, test_var, {'rate':RAND1, 'noise':RAND2}, [[
# 0.79172504, 0.52889492, 0.56804456, 0.92559664, 0.07103606, 0.0871293 , 0.0202184 , 0.83261985, 0.77815675, 0.87001215 ],[
# 1.3230471933615413, 1.4894230558066361, 1.3769970655058605, 1.3191168724311135, 1.1978884887731214, 1.4201278025008728, 1.2118209006969092, 1.6660066902162964, 1.737896449935246, 1.1576752082599944
#]]),
# (Functions.DictionaryMemory, test_var, {'rate':RAND1, 'noise':[RAND2], 'retrieval_prob':0.5},
# [[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]]),
# (Functions.DictionaryMemory, test_var, {'rate':RAND1, 'noise':RAND2, 'storage_prob':0.5},
# [[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]]),
# (Functions.DictionaryMemory, test_var, {'initializer':test_initializer, 'rate':RAND1, 'noise':RAND2}, [[
# 0.79172504, 0.52889492, 0.56804456, 0.92559664, 0.07103606, 0.0871293 , 0.0202184 , 0.83261985, 0.77815675, 0.87001215 ],[
# 1.3230471933615413, 1.4894230558066361, 1.3769970655058605, 1.3191168724311135, 1.1978884887731214, 1.4201278025008728, 1.2118209006969092, 1.6660066902162964, 1.737896449935246, 1.1576752082599944
#]]),
pytest.param(Functions.ContentAddressableMemory, test_var, {'rate':RAND1, 'retrieval_prob':0.5, 'seed': module_seed},
[[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]],
id="ContentAddressableMemory Low Retrieval"),
pytest.param(Functions.ContentAddressableMemory, test_var, {'rate':RAND1, 'storage_prob':0.1, 'seed': module_seed},
[[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]],
id="ContentAddressableMemory Low Storage"),
pytest.param(Functions.ContentAddressableMemory, test_var, {'rate':RAND1, 'retrieval_prob':0.9, 'storage_prob':0.9, 'seed': module_seed},
[[0.5488135039273248, 0.7151893663724195, 0.6027633760716439, 0.5448831829968969, 0.4236547993389047, 0.6458941130666561, 0.4375872112626925, 0.8917730007820798, 0.9636627605010293, 0.3834415188257777],
[0.7917250380826646, 0.5288949197529045, 0.5680445610939323, 0.925596638292661, 0.07103605819788694, 0.08712929970154071, 0.02021839744032572, 0.832619845547938, 0.7781567509498505, 0.8700121482468192]],
id="ContentAddressableMemory High Storage/Retrieval"),
pytest.param(Functions.ContentAddressableMemory, test_var, {'initializer':test_initializer, 'rate':RAND1, 'seed': module_seed},
[[0.5488135039273248, 0.7151893663724195, 0.6027633760716439, 0.5448831829968969, 0.4236547993389047, 0.6458941130666561, 0.4375872112626925, 0.8917730007820798, 0.9636627605010293, 0.3834415188257777],
[0.7917250380826646, 0.5288949197529045, 0.5680445610939323, 0.925596638292661, 0.07103605819788694, 0.08712929970154071, 0.02021839744032572, 0.832619845547938, 0.7781567509498505, 0.8700121482468192]],
id="ContentAddressableMemory Initializer"),
pytest.param(Functions.DictionaryMemory, philox_var, {'seed': module_seed},
[[0.45615033221654855, 0.5684339488686485, 0.018789800436355142, 0.6176354970758771, 0.6120957227224214, 0.6169339968747569, 0.9437480785146242, 0.6818202991034834, 0.359507900573786, 0.43703195379934145],
[0.6976311959272649, 0.06022547162926983, 0.6667667154456677, 0.6706378696181594, 0.2103825610738409, 0.1289262976548533, 0.31542835092418386, 0.3637107709426226, 0.5701967704178796, 0.43860151346232035]],
id="DictionaryMemory Philox"),
pytest.param(Functions.DictionaryMemory, philox_var, {'rate':RAND1, 'seed': module_seed},
[[0.45615033221654855, 0.5684339488686485, 0.018789800436355142, 0.6176354970758771, 0.6120957227224214, 0.6169339968747569, 0.9437480785146242, 0.6818202991034834, 0.359507900573786, 0.43703195379934145],
[0.6976311959272649, 0.06022547162926983, 0.6667667154456677, 0.6706378696181594, 0.2103825610738409, 0.1289262976548533, 0.31542835092418386, 0.3637107709426226, 0.5701967704178796, 0.43860151346232035]],
id="DictionaryMemory Rate Philox"),
pytest.param(Functions.DictionaryMemory, philox_var, {'initializer':test_initializer, 'rate':RAND1, 'seed': module_seed},
[[0.45615033221654855, 0.5684339488686485, 0.018789800436355142, 0.6176354970758771, 0.6120957227224214, 0.6169339968747569, 0.9437480785146242, 0.6818202991034834, 0.359507900573786, 0.43703195379934145],
[0.6976311959272649, 0.06022547162926983, 0.6667667154456677, 0.6706378696181594, 0.2103825610738409, 0.1289262976548533, 0.31542835092418386, 0.3637107709426226, 0.5701967704178796, 0.43860151346232035]],
id="DictionaryMemory Initializer Philox"),
pytest.param(Functions.DictionaryMemory, philox_var, {'rate':RAND1, 'retrieval_prob':0.01, 'seed': module_seed},
[[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]],
id="DictionaryMemory Low Retrieval Philox"),
pytest.param(Functions.DictionaryMemory, philox_var, {'rate':RAND1, 'storage_prob':0.01, 'seed': module_seed},
[[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]],
id="DictionaryMemory Low Storage Philox"),
pytest.param(Functions.DictionaryMemory, philox_var, {'rate':RAND1, 'retrieval_prob':0.95, 'storage_prob':0.95, 'seed': module_seed},
[[0.45615033221654855, 0.5684339488686485, 0.018789800436355142, 0.6176354970758771, 0.6120957227224214, 0.6169339968747569, 0.9437480785146242, 0.6818202991034834, 0.359507900573786, 0.43703195379934145],
[0.6976311959272649, 0.06022547162926983, 0.6667667154456677, 0.6706378696181594, 0.2103825610738409, 0.1289262976548533, 0.31542835092418386, 0.3637107709426226, 0.5701967704178796, 0.43860151346232035]],
id="DictionaryMemory High Storage/Retrieve Philox"),
# Disable noise tests for now as they trigger failure in DictionaryMemory lookup
# (Functions.DictionaryMemory, philox_var, {'rate':RAND1, 'noise':RAND2}, [[
# 0.79172504, 0.52889492, 0.56804456, 0.92559664, 0.07103606, 0.0871293 , 0.0202184 , 0.83261985, 0.77815675, 0.87001215 ],[
# 1.3230471933615413, 1.4894230558066361, 1.3769970655058605, 1.3191168724311135, 1.1978884887731214, 1.4201278025008728, 1.2118209006969092, 1.6660066902162964, 1.737896449935246, 1.1576752082599944
#]]),
# (Functions.DictionaryMemory, philox_var, {'rate':RAND1, 'noise':[RAND2], 'retrieval_prob':0.5},
# [[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]]),
# (Functions.DictionaryMemory, philox_var, {'rate':RAND1, 'noise':RAND2, 'storage_prob':0.5},
# [[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]]),
# (Functions.DictionaryMemory, philox_var, {'initializer':test_initializer, 'rate':RAND1, 'noise':RAND2}, [[
# 0.79172504, 0.52889492, 0.56804456, 0.92559664, 0.07103606, 0.0871293 , 0.0202184 , 0.83261985, 0.77815675, 0.87001215 ],[
# 1.3230471933615413, 1.4894230558066361, 1.3769970655058605, 1.3191168724311135, 1.1978884887731214, 1.4201278025008728, 1.2118209006969092, 1.6660066902162964, 1.737896449935246, 1.1576752082599944
#]]),
pytest.param(Functions.ContentAddressableMemory, philox_var, {'rate':RAND1, 'retrieval_prob':0.1, 'seed': module_seed},
[[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]],
id="ContentAddressableMemory Low Retrieval Philox"),
pytest.param(Functions.ContentAddressableMemory, philox_var, {'rate':RAND1, 'storage_prob':0.01, 'seed': module_seed},
[[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]],
id="ContentAddressableMemory Low Storage Philox"),
pytest.param(Functions.ContentAddressableMemory, philox_var, {'rate':RAND1, 'retrieval_prob':0.9, 'storage_prob':0.9, 'seed': module_seed},
[[0.45615033221654855, 0.5684339488686485, 0.018789800436355142, 0.6176354970758771, 0.6120957227224214, 0.6169339968747569, 0.9437480785146242, 0.6818202991034834, 0.359507900573786, 0.43703195379934145],
[0.6976311959272649, 0.06022547162926983, 0.6667667154456677, 0.6706378696181594, 0.2103825610738409, 0.1289262976548533, 0.31542835092418386, 0.3637107709426226, 0.5701967704178796, 0.43860151346232035]],
id="ContentAddressableMemory High Storage/Retrieval Philox"),
pytest.param(Functions.ContentAddressableMemory, philox_var, {'initializer':test_initializer, 'rate':RAND1, 'seed': module_seed},
[[0.45615033221654855, 0.5684339488686485, 0.018789800436355142, 0.6176354970758771, 0.6120957227224214, 0.6169339968747569, 0.9437480785146242, 0.6818202991034834, 0.359507900573786, 0.43703195379934145],
[0.6976311959272649, 0.06022547162926983, 0.6667667154456677, 0.6706378696181594, 0.2103825610738409, 0.1289262976548533, 0.31542835092418386, 0.3637107709426226, 0.5701967704178796, 0.43860151346232035]],
id="ContentAddressableMemory Initializer Philox"),
]
@pytest.mark.function
@pytest.mark.memory_function
@pytest.mark.benchmark
@pytest.mark.parametrize("func, variable, params, expected", test_data)
def test_basic(func, variable, params, expected, benchmark, func_mode):
if func is Functions.Buffer and func_mode != 'Python':
pytest.skip("Not implemented")
if func is Functions.ContentAddressableMemory and func_mode != 'Python':
pytest.skip("Not implemented")
benchmark.group = func.componentName
f = func(default_variable=variable, **params)
if variable is philox_var:
f.parameters.random_state.set(_SeededPhilox([module_seed]))
EX = pytest.helpers.get_func_execution(f, func_mode)
EX(variable)
res = benchmark(EX, variable)
assert np.allclose(res[0], expected[0])
assert np.allclose(res[1], expected[1])
#endregion
# **********************************************************************************************************************
# TEST Dictionary Memory ***********************************************************************************************
# **********************************************************************************************************************
#region
class TestDictionaryMemory:
# Test of DictionaryMemory without LLVM:
def test_DictionaryMemory_with_initializer_and_key_size_same_as_val_size(self):
stimuli = {'A': [[1,2,3],[4,5,6]],
'B': [[8,9,10],[11,12,13]],
'C': [[1,2,3],[11,12,13]],
'D': [[1,2,3],[21,22,23]],
'E': [[9,8,4],[11,12,13]],
'F': [[10,10,30],[40,50,60]],
}
em = EpisodicMemoryMechanism(
content_size=3,
assoc_size=3,
function = DictionaryMemory(
seed=2,
initializer=np.array([stimuli['F'], stimuli['F']], dtype=object),
duplicate_keys=True,
equidistant_keys_select=RANDOM)
)
retrieved_keys=[]
for key in sorted(stimuli.keys()):
retrieved = [i for i in em.execute(stimuli[key])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
retrieved_keys.append(retrieved_key)
assert retrieved_keys == [['F'], ['A'], ['A'], ['C'], ['B'], ['F']]
# Run again to test re-initialization and random retrieval
em.function.reset(np.array([stimuli['A'], stimuli['F']], dtype=object))
retrieved_keys=[]
for key in sorted(stimuli.keys()):
retrieved = [i for i in em.execute(stimuli[key])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
retrieved_keys.append(retrieved_key)
assert retrieved_keys == [['A'], ['A'], ['A'], ['A'], ['B'], ['F']]
stim = 'C'
em.function.equidistant_keys_select = OLDEST
retrieved = [i for i in em.function.get_memory(stimuli[stim][0])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
assert retrieved_key == ['A']
em.function.equidistant_keys_select = NEWEST
retrieved = [i for i in em.function.get_memory(stimuli[stim][0])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
assert retrieved_key == ['D']
# Test that after allowing dups, warning is issued and memory with zeros is returned
em.function.duplicate_keys = False
stim = 'A'
text = r'More than one item matched key \(\[1. 2. 3.\]\) in memory for DictionaryMemory'
with pytest.warns(UserWarning, match=text):
retrieved = em.execute(stimuli[stim])
retrieved_key = [k for k,v in stimuli.items() if v==list(retrieved)] or [None]
assert retrieved_key == [None]
assert retrieved[0] == [0, 0, 0]
assert retrieved[1] == [0, 0, 0]
def test_DictionaryMemory_with_initializer_and_key_size_diff_from_val_size(self):
stimuli = {'A': [[1,2,3],[4,5,6,7]],
'B': [[8,9,10],[11,12,13,14]],
'C': [[1,2,3],[11,12,13,14]],
'D': [[1,2,3],[21,22,23,24]],
'E': [[9,8,4],[11,12,13,14]],
'F': [[10,10,30],[40,50,60,70]],
}
em = EpisodicMemoryMechanism(
content_size=3,
assoc_size=4,
function = DictionaryMemory(
initializer=np.array([stimuli['F'], stimuli['F']], dtype=object),
duplicate_keys=True,
equidistant_keys_select=RANDOM,
seed=module_seed)
)
retrieved_keys=[]
for key in sorted(stimuli.keys()):
print(key)
retrieved = [i for i in em.execute(stimuli[key])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
retrieved_keys.append(retrieved_key)
assert retrieved_keys == [['F'], ['A'], ['A'], ['A'], ['B'], ['F']]
stim = 'C'
em.function.equidistant_keys_select = OLDEST
retrieved = [i for i in em.function.get_memory(stimuli[stim][0])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
assert retrieved_key == ['A']
em.function.equidistant_keys_select = NEWEST
retrieved = [i for i in em.function.get_memory(stimuli[stim][0])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
assert retrieved_key == ['D']
# Test that after allowing dups, warning is issued and memory with zeros is returned
em.function.duplicate_keys = False
stim = 'A'
text = r'More than one item matched key \(\[1. 2. 3.\]\) in memory for DictionaryMemory'
with pytest.warns(UserWarning, match=text):
retrieved = em.execute(stimuli[stim])
retrieved_key = [k for k,v in stimuli.items() if v==list(retrieved)] or [None]
assert retrieved_key == [None]
assert retrieved[0] == [0, 0, 0]
assert retrieved[1] == [0, 0, 0, 0]
# def test_DictionaryMemory_without_initializer_in_composition():
#
# content = TransferMechanism(size=5)
# assoc = TransferMechanism(size=3)
# content_out = TransferMechanism(size=5)
# assoc_out = TransferMechanism(size=3)
#
# # Episodic Memory, Decision and Control
# em = EpisodicMemoryMechanism(name='EM',
# content_size=5, assoc_size=3)
# comp = Composition()
# comp.add_nodes([content, assoc, content_out, assoc_out, em])
# comp.add_projection(MappingProjection(), content, em.input_ports[KEY_INPUT])
# comp.add_projection(MappingProjection(), assoc, em.input_ports[VALUE_INPUT])
# comp.add_projection(MappingProjection(), em.output_ports[KEY_OUTPUT], content_out)
# comp.add_projection(MappingProjection(), em.output_ports[VALUE_OUTPUT], assoc_out)
#
# comp.run(inputs={content:[1,2,3,4,5],
# assoc:[6,7,8]})
def test_DictionaryMemory_without_initializer_and_key_size_same_as_val_size(self):
stimuli = {'A': [[1,2,3],[4,5,6]],
'B': [[8,9,10],[11,12,13]],
'C': [[1,2,3],[11,12,13]],
'D': [[1,2,3],[21,22,23]],
'E': [[9,8,4],[11,12,13]],
'F': [[10,10,30],[40,50,60]],
}
em = EpisodicMemoryMechanism(
content_size=3,
assoc_size=3,
function = DictionaryMemory(
duplicate_keys=True,
equidistant_keys_select=RANDOM,
seed=module_seed)
)
retrieved_keys=[]
for key in sorted(stimuli.keys()):
retrieved = [i for i in em.execute(stimuli[key])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
retrieved_keys.append(retrieved_key)
assert retrieved_keys == [[None], ['A'], ['A'], ['C'], ['B'], ['D']]
stim = 'C'
em.function.equidistant_keys_select = OLDEST
retrieved = [i for i in em.function.get_memory(stimuli[stim][0])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
assert retrieved_key == ['A']
em.function.equidistant_keys_select = NEWEST
retrieved = [i for i in em.function.get_memory(stimuli[stim][0])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
assert retrieved_key == ['D']
# Test that after allowing dups, warning is issued and memory with zeros is returned
em.function.duplicate_keys = False
stim = 'A'
text = r'More than one item matched key \(\[1. 2. 3.\]\) in memory for DictionaryMemory'
with pytest.warns(UserWarning, match=text):
retrieved = em.execute(stimuli[stim])
retrieved_key = [k for k,v in stimuli.items() if v==list(retrieved)] or [None]
assert retrieved_key == [None]
assert retrieved[0] == [0, 0, 0]
assert retrieved[1] == [0, 0, 0]
def test_DictionaryMemory_without_initializer_and_key_size_diff_from_val_size(self):
stimuli = {'A': [[1,2,3],[4,5,6,7]],
'B': [[8,9,10],[11,12,13,14]],
'C': [[1,2,3],[11,12,13,14]],
'D': [[1,2,3],[21,22,23,24]],
'E': [[9,8,4],[11,12,13,14]],
'F': [[10,10,30],[40,50,60,70]],
}
em = EpisodicMemoryMechanism(
content_size=3,
assoc_size=4,
function = DictionaryMemory(
duplicate_keys=True,
equidistant_keys_select=RANDOM,
seed=module_seed)
)
retrieved_keys=[]
for key in sorted(stimuli.keys()):
retrieved = [i for i in em.execute(stimuli[key])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
retrieved_keys.append(retrieved_key)
assert retrieved_keys == [[None], ['A'], ['A'], ['C'], ['B'], ['D']]
stim = 'C'
em.function.equidistant_keys_select = OLDEST
retrieved = [i for i in em.function.get_memory(stimuli[stim][0])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
assert retrieved_key == ['A']
em.function.equidistant_keys_select = NEWEST
retrieved = [i for i in em.function.get_memory(stimuli[stim][0])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
assert retrieved_key == ['D']
# Test that after allowing dups, warning is issued and memory with zeros is returned
em.function.duplicate_keys = False
stim = 'A'
text = r'More than one item matched key \(\[1. 2. 3.\]\) in memory for DictionaryMemory'
with pytest.warns(UserWarning, match=text):
retrieved = em.execute(stimuli[stim])
retrieved_key = [k for k,v in stimuli.items() if v==list(retrieved)] or [None]
assert retrieved_key == [None]
assert retrieved[0] == [0, 0, 0]
assert retrieved[1] == [0, 0, 0, 0]
def test_DictionaryMemory_without_assoc(self):
stimuli = {'A': [[1,2,3]],
'B': [[8,9,10]],
'C': [[1,2,3]],
'D': [[1,2,3]],
'E': [[9,8,4]],
'F': [[10,10,30]],
}
em = EpisodicMemoryMechanism(
name='EPISODIC MEMORY MECH',
content_size=3,
function = DictionaryMemory(
# initializer=np.array([stimuli['F'], stimuli['F']], dtype=object),
duplicate_keys=True,
equidistant_keys_select=RANDOM,
retrieval_prob = 1.0,
seed=module_seed,
)
)
for key in sorted(stimuli.keys()):
print(f'\nCurrent memory: \n{em.memory}\n')
retrieved = [i for i in em.execute(stimuli[key])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
print(f'\nExecuted with stimulus {key}: {stimuli[key]};'
f'\nRetrieved memory {retrieved_key[0]}: \n\t{retrieved}')
retrieved_keys=[]
for key in sorted(stimuli.keys()):
retrieved = [i for i in em.execute(stimuli[key])]
retrieved_key = [k for k,v in stimuli.items() if v == retrieved] or [None]
retrieved_keys.append(retrieved_key)
assert retrieved_keys == [['A', 'C', 'D'], ['B'], ['A', 'C', 'D'], ['A', 'C', 'D'], ['E'], ['F']]
def test_DictionaryMemory_with_duplicate_entry_in_initializer_warning(self):
regexp = r'Attempt to initialize memory of DictionaryMemory with an entry \([[1 2 3]'
with pytest.warns(UserWarning, match=regexp):
em = EpisodicMemoryMechanism(
name='EPISODIC MEMORY MECH',
content_size=3,
assoc_size=3,
function = DictionaryMemory(
initializer=np.array([[[1,2,3], [4,5,6]],
[[1,2,3], [7,8,9]]]),
duplicate_keys=False,
equidistant_keys_select=RANDOM,
retrieval_prob = 1.0,
seed=module_seed,
)
)
assert np.allclose(em.memory, np.array([[[1, 2, 3], [4, 5, 6]]]))
def test_DictionaryMemory_add_and_delete_from_memory(self):
em = DictionaryMemory(
initializer=[[[1,2,3], [4,5,6]],
[[7,8,9], [10,11,12]]],
duplicate_keys=True,
equidistant_keys_select=RANDOM,
retrieval_prob = 1.0,
storage_prob = 1.0,
seed=module_seed,
)
em.add_to_memory([[[10,20,30],[40,50,60]],
[[11,21,31],[41,51,61]]])
expected_memory = [[[ 1, 2, 3],[ 4, 5, 6]],
[[ 7, 8, 9],[10, 11, 12]],
[[10, 20, 30],[40, 50, 60]],
[[11, 21, 31],[41, 51, 61]]]
assert np.allclose(em.memory, expected_memory)
em.delete_from_memory([[[1,2,3],[4,5,6]]])
expected_memory = [[[ 7, 8, 9],[10, 11, 12]],
[[10, 20, 30],[40, 50, 60]],
[[11, 21, 31],[41, 51, 61]]]
assert np.allclose(em.memory, expected_memory)
# Test adding and deleting a single memory
em.add_to_memory([[1,2,3],[100,101,102]])
expected_memory = [[[ 7, 8, 9],[10, 11, 12]],
[[10, 20, 30],[40, 50, 60]],
[[11, 21, 31],[41, 51, 61]],
[[ 1, 2, 3],[100,101,102]]]
assert np.allclose(em.memory, expected_memory)
em.delete_from_memory([[1,2,3],[100,101,102]])
expected_memory = [[[ 7, 8, 9],[10, 11, 12]],
[[10, 20, 30],[40, 50, 60]],
[[11, 21, 31],[41, 51, 61]]]
assert np.allclose(em.memory, expected_memory)
# Test adding memory with different size value
em.add_to_memory([[1,2,3],[100,101,102,103]])
expected_memory = [[[ 7, 8, 9],[10, 11, 12]],
[[10, 20, 30],[40, 50, 60]],
[[11, 21, 31],[41, 51, 61]],
[[ 1, 2, 3],[100,101,102,103]]]
for m,e in zip(em.memory,expected_memory):
for i,j in zip(m,e):
assert np.allclose(i,j)
# Test adding memory with different size value as np.array
em.add_to_memory(np.array([[1,2,3],[200,201,202,203]], dtype=object))
expected_memory = [[[ 7, 8, 9],[10, 11, 12]],
[[10, 20, 30],[40, 50, 60]],
[[11, 21, 31],[41, 51, 61]],
[[ 1, 2, 3],[100,101,102,103]],
[[ 1, 2, 3],[200,201,202,203]]]
for m,e in zip(em.memory,expected_memory):
for i,j in zip(m,e):
assert np.allclose(i,j)
# Test error for illegal key:
with pytest.raises(FunctionError) as error_text:
em.add_to_memory(np.array([[1,2],[200,201,202,203]], dtype=object))
assert "Length of 'key'" in str(error_text.value) and "must be same as others in the dict" in str(error_text.value)
def test_DictionaryMemory_overwrite_mode(self):
em = DictionaryMemory(
initializer=[[[1,2,3], [4,5,6]],
[[7,8,9], [10,11,12]]],
duplicate_keys=True,
equidistant_keys_select=RANDOM,
retrieval_prob = 1.0,
storage_prob = 1.0,
seed=module_seed,
)
em.duplicate_keys = OVERWRITE
# Add new memory
retreived = em.execute([[7,8,10], [100,110,120]])
assert np.allclose(list(retreived), [[7,8,9],[10,11,12]])
expected_memory = [[[ 1, 2, 3],[4, 5, 6]],
[[7,8,9], [10,11,12]],
[[7,8,10], [100,110,120]]]
assert np.allclose(em.memory, expected_memory)
# Overwrite old memory
retreived = em.execute([[7,8,9], [100,110,120]])
assert np.allclose(list(retreived), [[7,8,9],[10,11,12]])
expected_memory = [[[ 1, 2, 3],[4, 5, 6]],
[[7,8,9], [100,110,120]],
[[7,8,10], [100,110,120]]]
assert np.allclose(em.memory, expected_memory)
# Allow entry of memory with duplicate key
em.duplicate_keys = True
retreived = em.execute([[7,8,9], [200,210,220]])
assert np.allclose(list(retreived), [[7,8,9],[100,110,120]])
expected_memory = [[[ 1, 2, 3],[4, 5, 6]],
[[7,8,9], [100,110,120]],
[[7,8,10], [100,110,120]],
[[7,8,9], [200,210,220]]]
assert np.allclose(em.memory, expected_memory)
# Attempt to overwrite with two matches should generate error
em.duplicate_keys = OVERWRITE
with pytest.raises(FunctionError) as error_text:
em.execute([[7,8,9], [200,210,220]])
assert ('Attempt to store item' in str(error_text.value)
and 'with \'duplicate_keys\'=\'OVERWRITE\'' in str(error_text.value))
def test_DictionaryMemory_max_entries(self):
em = DictionaryMemory(
initializer=[[[1,2,3], [4,5,6]],
[[7,8,9], [10,11,12]],
[[1,2,3], [100,101,102]]],
duplicate_keys=True,
equidistant_keys_select=RANDOM,
retrieval_prob = 1.0,
storage_prob = 1.0,
max_entries = 4,
seed=module_seed,
)
em.add_to_memory([[[10,20,30],[40,50,60]],
[[11,21,31],[41,51,61]],
[[12,22,32],[42,52,62]]])
expected_memory = [[[1,2,3], [100,101,102]],
[[10,20,30],[40,50,60]],
[[11,21,31],[41,51,61]],
[[12,22,32],[42,52,62]]]
assert np.allclose(em.memory, expected_memory)
@pytest.mark.parametrize(
'param_name',
[
'distance_function',
'selection_function',
]
)
def test_DictionaryMemory_unique_functions(self, param_name):
a = DictionaryMemory()
b = DictionaryMemory()
assert (
getattr(a.parameters, param_name).get()
is not getattr(b.parameters, param_name).get()
)
assert (
getattr(a.defaults, param_name)
is not getattr(b.defaults, param_name)
)
#endregion
# **********************************************************************************************************************
# TEST ContentAddressableMemory ****************************************************************************************
# **********************************************************************************************************************
def retrieve_label_helper(retrieved, stimuli):
return [k for k,v in stimuli.items()
if all(np.alltrue(a)
for a in np.equal(np.array(retrieved, dtype=object),
np.array(v, dtype=object),
dtype=object))] or [None]
#region
class TestContentAddressableMemory:
# Note: this warning is issued because the default distance_function is Distance(metric=COSINE)
# if the default is changed, this warning may not occur
distance_warning_msg = "and has at least one memory field that is a scalar"
test_vars = [
# initializer: expected_result (as list):
(1, [[[1.]]], distance_warning_msg),
([1], [[[1.]]], distance_warning_msg),
([1,1], [[[1., 1.]]], None),
([[1,1]], [[[1., 1.]]], None),
([[[1,1]]], [[[1., 1.]]], None),
([[1,1],[2,2,2]], [[[1., 1.],[2., 2., 2.]]], None),
([[[1,1],[2,2,2]]], [[[1., 1.],[2., 2., 2.]]], None)
]
@pytest.mark.parametrize('initializer, expected_memory, warning_msg', test_vars)
def test_ContentAddressableMemory_allowable_initializer_shapes(self, initializer, expected_memory, warning_msg):
if warning_msg:
with pytest.warns(UserWarning, match=warning_msg):
c = ContentAddressableMemory(initializer=initializer)
np.all(c.memory==convert_all_elements_to_np_array(expected_memory))
else:
c = ContentAddressableMemory(initializer=initializer)
np.all(c.memory==convert_all_elements_to_np_array(expected_memory))
def test_ContentAddressableMemory_simple_distances(self):
stimuli = np.array([[[1,2,3],[4,5,6]],
[[1,2,5],[4,5,8]],
[[1,2,10],[4,5,10]]
])
c = ContentAddressableMemory(
initializer=stimuli,
storage_prob=0,
distance_function=Distance(metric=COSINE),
seed=module_seed,
)
# Test distance (for retrieved item) and distances_by_field
retrieved = c([[1,2,4],[4,5,9]])
assert np.all(retrieved==np.array([[1,2,5],[4,5,8]]))
assert c.distance == Distance(metric=COSINE)([retrieved,[[1,2,4],[4,5,9]]])
assert np.allclose(c.distances_by_field, [0.00397616, 0.00160159])
# Test distance_field_weights as scalar
c.distance_field_weights=[2.5]
retrieved = c([[1,2,4],[4,5,9]])
assert np.all(retrieved==np.array([[1,2,5],[4,5,8]]))
assert c.distance == 2.5 * Distance(metric=COSINE)([retrieved,[[1,2,4],[4,5,9]]])
assert np.allclose(c.distances_by_field, [2.5 * 0.00397616, 2.5 * 0.00160159])
# Test with 0 as field weight
c.distance_field_weights=[1,0]
retrieved = c([[1,2,3],[4,5,10]])
assert np.all(retrieved==np.array([[1,2,3],[4,5,6]]))
assert all(c.distances_by_field == [0.0, 0.0])
c.distance_field_weights=[0,1]
retrieved = c([[1,2,3],[4,5,10]])
assert np.all(retrieved==np.array([[1,2,10],[4,5,10]]))
assert all(c.distances_by_field == [0.0, 0.0])
# Test with None as field weight
c.distance_field_weights=[None,1]
retrieved = c([[1,2,3],[4,5,10]])
assert np.all(retrieved==np.array([[1,2,10],[4,5,10]]))
assert all(c.distances_by_field == [None, 0.0])
c.distance_field_weights=[1, None]
retrieved = c([[1,2,3],[4,5,10]])
assert np.all(retrieved==np.array([[1,2,3],[4,5,6]]))
assert all(c.distances_by_field == [0.0, None])
# FIX: COULD CONDENSE THESE TESTS BY PARAMETERIZING FIELD-WEIGHTS AND ALSO INCLUDE DISTANCE METRIC AS A PARAM
def test_ContentAddressableMemory_parametric_distances(self):
stimuli = np.array([[[1,2,3],[4,5,6]],
[[7,8,9],[10,11,12]],
[[13,14,15],[16,17,18]]])
c = ContentAddressableMemory(
initializer=stimuli,
storage_prob=0,
distance_function=Distance(metric=COSINE),
seed=module_seed,
)
pairs = list(combinations(range(0,3),2))
# Distances between all stimuli
distances = [Distance(metric=COSINE)([stimuli[i],stimuli[j]]) for i, j in pairs]
c_distances = []
# for i,j in pairs:
# Test distances with evenly weighted fields
retrieved = c(stimuli[0])
np.all(retrieved==stimuli[0])
assert np.allclose(c.distances_to_entries, [0, distances[0], distances[1]])
retrieved = c(stimuli[1])
np.all(retrieved==stimuli[1])
assert np.allclose(c.distances_to_entries, [distances[0], 0, distances[2]])
retrieved = c(stimuli[2])
np.all(retrieved==stimuli[2])
assert np.allclose(c.distances_to_entries, [distances[1], distances[2], 0])
# Test distances using distance_field_weights
field_weights = [np.array([[1],[0]]), np.array([[0],[1]])]
for fw in field_weights:
c.distance_field_weights = fw
distances = []
for k in range(2):
if fw[k]:
distances.append([Distance(metric=COSINE)([stimuli[i][k], stimuli[j][k]]) * fw[k]
for i, j in pairs])
distances = np.array(distances)
distances = np.squeeze(np.sum(distances, axis=0) / len([f for f in fw if f]))
retrieved = c(stimuli[0])
np.all(retrieved==stimuli[0])
assert np.allclose(c.distances_to_entries, [0, distances[0], distances[1]])
retrieved = c(stimuli[1])
np.all(retrieved==stimuli[1])
assert np.allclose(c.distances_to_entries, [distances[0], 0, distances[2]])
retrieved = c(stimuli[2])
np.all(retrieved==stimuli[2])
assert np.allclose(c.distances_to_entries, [distances[1], distances[2], 0])
# Test distances_by_fields
c.distance_field_weights=[1,1]
stim = [[8,9,10],[11,12,13]]
retrieved = c(stim)
np.all(retrieved==np.array([[7,8,9],[10,11,12]]))
distances_by_field = [Distance(metric=COSINE)([retrieved[i], stim[i]]) for i in range(2)]
np.allclose(c.distances_by_field, distances_by_field)
# Test of ContentAddressableMemory without LLVM:
def test_ContentAddressableMemory_with_initializer_and_equal_field_sizes(self):
stimuli = {'A': [[1,2,3],[4,5,6]],
'B': [[8,9,10],[11,12,13]],
'C': [[1,2,3],[11,12,13]],
'D': [[1,2,3],[21,22,23]],
'E': [[9,8,4],[11,12,13]],
'F': [[10,10,30],[40,50,60]],
}
c = ContentAddressableMemory(
seed=2,
initializer=np.array([stimuli['F'], stimuli['F']], dtype=object),
distance_function=Distance(metric=COSINE),
duplicate_entries_allowed=True,
equidistant_entries_select=RANDOM
)
retrieved_labels=[]
sorted_labels = sorted(stimuli.keys())
for label in sorted_labels:
retrieved = [i for i in c(stimuli[label])]
# Get label of retrieved item
retrieved_label = retrieve_label_helper(retrieved, stimuli)
# Get distances of retrieved entry to all other entries and assert it has the minimum distance
distances = [Distance(metric=COSINE)([retrieved,stimuli[k]]) for k in sorted_labels]
min_idx = distances.index(min(distances))
assert retrieved_label == [sorted_labels[min_idx]]
retrieved_labels.append(retrieved_label)
assert retrieved_labels == [['F'], ['A'], ['F'], ['C'], ['B'], ['F']]
# Run again to test re-initialization and random retrieval
c.reset(np.array([stimuli['A'], stimuli['F']], dtype=object))
retrieved_labels=[]
for label in sorted(stimuli.keys()):
retrieved = [i for i in c(stimuli[label])]
retrieved_label = retrieve_label_helper(retrieved, stimuli)
# Get distances of retrieved entry to all other entries and assert it has the minimum distance
distances = [Distance(metric=COSINE)([retrieved,stimuli[k]]) for k in sorted_labels]
min_idx = distances.index(min(distances))
assert retrieved_label == [sorted_labels[min_idx]]
retrieved_labels.append(retrieved_label)
Distance(metric=COSINE)([retrieved,stimuli['A']])
assert retrieved_labels == [['A'], ['A'], ['F'], ['C'], ['B'], ['F']]
# Test restricting retrieval to only 1st field (which has duplicate values) and selecting for OLDEST
c.distance_field_weights = [1,0]
stim = 'C' # Has same 1st field as A (older) and D (newer)
c.equidistant_entries_select = OLDEST # Should return A
retrieved = c.get_memory(stimuli[stim])
retrieved_label = [k for k,v in stimuli.items()
if np.all([v[i] == retrieved[i] for i in range(len(v))])] or [None]
assert retrieved_label == ['A']
c.equidistant_entries_select = NEWEST # Should return D
retrieved = c.get_memory(stimuli[stim])
retrieved_label = retrieve_label_helper(retrieved, stimuli)
assert retrieved_label == ['D']
# Test that after allowing dups and now disallowing them, warning is issued and memory with zeros is returned
c.duplicate_entries_allowed = False
stim = 'A'
text = "More than one entry matched cue"
with pytest.warns(UserWarning, match=text):
retrieved = c(stimuli[stim])
retrieved_label = retrieve_label_helper(retrieved, stimuli)
assert retrieved_label == [None]
expected = np.array([[0,0,0],[0,0,0]])
assert np.all(expected==retrieved)
def test_ContentAddressableMemory_with_initializer_and_diff_field_sizes(self):
stimuli = {'A': np.array([[1.,2.,3.],[4.,5.,6.,7.]], dtype=object),
'B': np.array([[8.,9.,10.],[11.,12.,13.,14.]], dtype=object),
'C': np.array([[1.,2.,3.],[11.,12.,13.,14.]], dtype=object),
'D': np.array([[1.,2.,3.],[21.,22.,23.,24.]], dtype=object),
'E': np.array([[9.,8.,4.],[11.,12.,13.,14.]], dtype=object),
'F': np.array([[10.,10.,30.],[40.,50.,60.,70.]], dtype=object),
}
c = ContentAddressableMemory(
initializer=np.array([stimuli['F'], stimuli['F']], dtype=object),
duplicate_entries_allowed=True,
equidistant_entries_select=RANDOM,
seed=module_seed,
)
# Run again to test re-initialization and random retrieval
c.reset(np.array([stimuli['A'], stimuli['F']], dtype=object))
retrieved_labels=[]
for key in sorted(stimuli.keys()):
retrieved = c(stimuli[key])
retrieved_label = retrieve_label_helper(retrieved, stimuli)
retrieved_labels.append(retrieved_label)
assert retrieved_labels == [['A'], ['A'], ['F'], ['C'], ['B'], ['F']]
c.distance_field_weights = [1,0]
stim = 'C'
c.equidistant_entries_select = OLDEST
retrieved = c.get_memory(stimuli[stim])
retrieved_label = retrieve_label_helper(retrieved, stimuli)
retrieved_labels.append(retrieved_label)
assert retrieved_label == ['A']
c.equidistant_entries_select = NEWEST
retrieved = c.get_memory(stimuli[stim])
retrieved_label = retrieve_label_helper(retrieved, stimuli)
assert retrieved_label == ['D']
# Test that after allowing dups, warning is issued and memory with zeros is returned
c.duplicate_entries_allowed = False
stim = 'A'
text = r'More than one entry matched cue'
with pytest.warns(UserWarning, match=text):
retrieved = c(stimuli[stim])
retrieved_label = retrieve_label_helper(retrieved, stimuli)
assert retrieved_label == [None]
expected = np.array([np.array([0,0,0]),np.array([0,0,0,0])], dtype=object)
retrieved = np.array(retrieved, dtype=object)
assert np.all(np.alltrue(x) for x in np.equal(expected,retrieved, dtype=object))
def test_ContentAddressableMemory_without_initializer_and_equal_field_sizes(self):
stimuli = {'A': [[1,2,3],[4,5,6]],
'B': [[8,9,10],[11,12,13]],
'C': [[1,2,3],[11,12,13]],
'D': [[1,2,3],[21,22,23]],
'E': [[9,8,4],[11,12,13]],
'F': [[10,10,30],[40,50,60]],
}
c = ContentAddressableMemory(
distance_function=Distance(metric=COSINE),
duplicate_entries_allowed=True,
equidistant_entries_select=RANDOM,
seed=module_seed,
)
retrieved_labels=[]
sorted_labels = sorted(stimuli.keys())
for label in sorted_labels:
retrieved = [i for i in c(stimuli[label])]
retrieved_label = retrieve_label_helper(retrieved, stimuli)
retrieved_labels.append(retrieved_label)
assert retrieved_labels == [[None], ['A'], ['A'], ['C'], ['B'], ['A']]
stim = 'C'
c.distance_field_weights = [1,0]
c.equidistant_entries_select = OLDEST
retrieved = [i for i in c.get_memory(stimuli[stim])]
retrieved_label = retrieve_label_helper(retrieved, stimuli)
assert retrieved_label == ['A']
c.equidistant_entries_select = NEWEST
retrieved = [i for i in c.get_memory(stimuli[stim])]
retrieved_label = retrieve_label_helper(retrieved, stimuli)
assert retrieved_label == ['D']
# Test that after allowing dups, warning is issued and memory with zeros is returned
c.duplicate_entries_allowed = False
stim = 'A'
text = "More than one entry matched cue"
with pytest.warns(UserWarning, match=text):
retrieved = c.execute(stimuli[stim])
retrieved_label = retrieve_label_helper(retrieved, stimuli)
assert retrieved_label == [None]
expected = np.array([np.array([0,0,0]),np.array([0,0,0])])
assert all(np.alltrue(x) for x in np.equal(expected,retrieved, dtype=object))
def test_ContentAddressableMemory_without_initializer_and_diff_field_sizes(self):
stimuli = {'A': np.array([[1,2,3],[4,5,6,7]], dtype=object),
'B': np.array([[8,9,10],[11,12,13,14]], dtype=object),
'C': np.array([[1,2,3],[11,12,13,14]], dtype=object),
'D': np.array([[1,2,3],[21,22,23,24]], dtype=object),
'E': np.array([[9,8,4],[11,12,13,14]], dtype=object),
'F': np.array([[10,10,30],[40,50,60,70]], dtype=object),
}
c = ContentAddressableMemory(
duplicate_entries_allowed=True,
equidistant_entries_select=RANDOM,
distance_field_weights=[1,0],
seed=module_seed,
)
retrieved_labels=[]
for key in sorted(stimuli.keys()):
retrieved = c(stimuli[key])
retrieved_label = retrieve_label_helper(retrieved, stimuli)
retrieved_labels.append(retrieved_label)
assert retrieved_labels == [[None], ['A'], ['A'], ['C'], ['B'], ['D']]
stim = 'C'
c.equidistant_entries_select = OLDEST
retrieved = c.get_memory(stimuli[stim])
retrieved_label = retrieve_label_helper(retrieved, stimuli)
assert retrieved_label == ['A']
c.equidistant_entries_select = NEWEST
retrieved = c.get_memory(stimuli[stim])
retrieved_label = retrieve_label_helper(retrieved, stimuli)
assert retrieved_label == ['D']
# Test that after allowing dups, warning is issued and memory with zeros is returned
c.duplicate_entries_allowed = False
stim = 'A'
text = "More than one entry matched cue"
with pytest.warns(UserWarning, match=text):
retrieved = c(stimuli[stim])
retrieved_label = retrieve_label_helper(retrieved, stimuli)
assert retrieved_label == [None]
expected = np.array([np.array([0,0,0]),np.array([0,0,0,0])], dtype=object)
retrieved = np.array(retrieved, dtype=object)
assert np.all(np.alltrue(np.array(x) for x in np.equal(expected, retrieved, dtype=object)))
def test_ContentAddressableMemory_with_duplicate_entry_in_initializer_warning(self):
regexp = r'Attempt to initialize memory of ContentAddressableMemory with an entry \([[1 2 3]'
with pytest.warns(UserWarning, match=regexp):
c = ContentAddressableMemory(
initializer=np.array([[[1,2,3], [4,5,6]],
[[1,2,3], [7,8,9]]]),
duplicate_entries_allowed=False,
distance_field_weights=[1,0],
equidistant_entries_select=RANDOM,
retrieval_prob = 1.0,
seed=module_seed,
)