def test_subset(rng):
v1 = Vocabulary(32, rng=rng)
v1.parse('A+B+C+D+E+F+G')
# Test creating a vocabulary subset
v2 = v1.create_subset(['A', 'C', 'E'])
assert v2.keys == ['A', 'C', 'E']
assert v2['A'] == v1['A']
assert v2['C'] == v1['C']
assert v2['E'] == v1['E']
assert v2.parent is v1
# Test creating a subset from a subset (it should create off the parent)
v3 = v2.create_subset(['C', 'E'])
assert v3.parent is v2.parent and v2.parent is v1
v3.include_pairs = True
assert v3.key_pairs == ['C*E']
assert not v1.include_pairs
assert not v2.include_pairs
# Test transform_to between subsets (should be identity transform)
t = v1.transform_to(v2)
assert v2.parse('A').compare(np.dot(t, v1.parse('A').v)) >= 0.99999999
def test_subset(rng):
v1 = Vocabulary(32, rng=rng)
v1.parse("A+B+C+D+E+F+G")
# Test creating a vocabulary subset
v2 = v1.create_subset(["A", "C", "E"])
assert v2.keys == ["A", "C", "E"]
assert v2["A"] == v1["A"]
assert v2["C"] == v1["C"]
assert v2["E"] == v1["E"]
assert v2.parent is v1
# Test creating a subset from a subset (it should create off the parent)
v3 = v2.create_subset(["C", "E"])
assert v3.parent is v2.parent and v2.parent is v1
v3.include_pairs = True
assert v3.key_pairs == ["C*E"]
assert not v1.include_pairs
assert not v2.include_pairs
# Test transform_to between subsets (should be identity transform)
t = v1.transform_to(v2)
assert v2.parse("A").compare(np.dot(t, v1.parse("A").v)) >= 0.99999999
class SpaunVocabulary(object):
def __init__(self):
self.main = None
self.sp_dim = 512
self.mtr_dim = 50
self.vis_dim = 200
# ############ Semantic pointer (strings) definitions #################
# --- Numerical semantic pointers ---
self.num_sp_strs = ['ZER', 'ONE', 'TWO', 'THR', 'FOR',
'FIV', 'SIX', 'SEV', 'EIG', 'NIN']
self.n_num_sp = len(self.num_sp_strs)
# --- Task semantic pointer list ---
# W - Drawing (Copying visual input)
# R - Recognition
# L - Learning (Bandit Task)
# M - Memory (forward serial recall)
# C - Counting
# A - Answering
# V - Rapid Variable Creation
# F - Fluid Induction (Ravens)
# X - Task precursor
# DEC - Decoding task (output to motor system)
self.ps_task_sp_strs = ['W', 'R', 'L', 'M', 'C', 'A', 'V', 'F', 'X',
'DEC']
self.ps_task_vis_sp_strs = ['A', 'C', 'F', 'K', 'L', 'M', 'P', 'R',
'V', 'W']
# --- Task visual semantic pointer usage ---
# A - Task initialization
# F - Forward recall
# R - Reverse recall
# K - Q&A 'kind' probe
# P - Q&A 'position' probe
# --- Production system semantic pointers ---
# DECW - Decoding state (output to motor system, but for drawing task)
# DECI - Decoding state (output to motor system, but for inductn tasks)
self.ps_state_sp_strs = ['QAP', 'QAK', 'TRANS0', 'TRANS1', 'TRANS2',
'CNT0', 'CNT1', 'LEARN']
self.ps_dec_sp_strs = ['FWD', 'REV', 'CNT', 'DECW', 'DECI', 'NONE']
# --- Misc actions semantic pointers
self.ps_action_sp_strs = None
self.min_num_ps_actions = 3
# --- Misc visual semantic pointers ---
self.misc_vis_sp_strs = ['OPEN', 'CLOSE', 'SPACE', 'QM']
# --- Misc state semantic pointers ---
self.misc_ps_sp_strs = ['MATCH', 'NO_MATCH']
# --- 'I don't know' motor response vector
self.mtr_sp_strs = ['UNK']
# --- List of all visual semantic pointers ---
self.vis_sp_strs = list(self.num_sp_strs)
self.vis_sp_strs.extend(self.misc_vis_sp_strs)
self.vis_sp_strs.extend(self.ps_task_vis_sp_strs)
# --- Position (enumerated) semantic pointers ---
self.pos_sp_strs = None
self.max_enum_list_pos = 8
# --- Operations semantic pointers
self.ops_sp_strs = ['ADD', 'INC']
# --- Reward semantic pointers
self.reward_n_sp_str = self.num_sp_strs[0]
self.reward_y_sp_str = self.num_sp_strs[1]
self.reward_sp_strs = [self.reward_n_sp_str, self.reward_y_sp_str]
def write_header(self):
logger.write('# Spaun Vocabulary Options:\n')
logger.write('# -------------------------\n')
for param_name in sorted(self.__dict__.keys()):
param_value = getattr(self, param_name)
if not callable(param_value) and not isinstance(param_value, list)\
and not isinstance(param_value, Vocabulary) \
and not isinstance(param_value, SemanticPointer) \
and not isinstance(param_value, np.ndarray):
logger.write('# - %s = %s\n' % (param_name, param_value))
logger.write('\n')
def initialize(self, num_learn_actions=3, rng=0):
if rng == 0:
rng = np.random.RandomState(int(time.time()))
# ############### Semantic pointer list definitions ###################
# --- Position (enumerated) semantic pointers ---
self.pos_sp_strs = ['POS%i' % (i + 1)
for i in range(self.max_enum_list_pos)]
# --- Unitary semantic pointers
self.unitary_sp_strs = [self.num_sp_strs[0], self.pos_sp_strs[0]]
self.unitary_sp_strs.extend(self.ops_sp_strs)
# --- Production system (action) semantic pointers ---
self.ps_action_learn_sp_strs = ['A%d' % (i + 1) for i in
range(num_learn_actions)]
self.ps_action_misc_sp_strs = []
self.ps_action_sp_strs = (self.ps_action_learn_sp_strs +
self.ps_action_misc_sp_strs)
# #################### Vocabulary definitions #########################
# --- Primary vocabulary ---
self.main = Vocabulary(self.sp_dim, unitary=self.unitary_sp_strs,
rng=rng)
# --- Add numerical sp's ---
self.main.parse('%s+%s' % (self.ops_sp_strs[0], self.num_sp_strs[0]))
add_sp = self.main[self.ops_sp_strs[0]]
num_sp = self.main[self.num_sp_strs[0]].copy()
for i in range(len(self.num_sp_strs) - 1):
num_sp = num_sp.copy() * add_sp
self.main.add(self.num_sp_strs[i + 1], num_sp)
self.add_sp = add_sp
# --- Add positional sp's ---
self.main.parse('%s+%s' % (self.ops_sp_strs[1], self.pos_sp_strs[0]))
inc_sp = self.main[self.ops_sp_strs[1]]
pos_sp = self.main[self.pos_sp_strs[0]].copy()
for i in range(len(self.pos_sp_strs) - 1):
pos_sp = pos_sp.copy() * inc_sp
self.main.add(self.pos_sp_strs[i + 1], pos_sp)
self.inc_sp = inc_sp
# --- Add other visual sp's ---
self.main.parse('+'.join(self.misc_vis_sp_strs))
self.main.parse('+'.join(self.ps_task_vis_sp_strs))
# --- Add production system sp's ---
self.main.parse('+'.join(self.ps_task_sp_strs))
self.main.parse('+'.join(self.ps_state_sp_strs))
self.main.parse('+'.join(self.ps_dec_sp_strs))
if len(self.ps_action_sp_strs) > 0:
self.main.parse('+'.join(self.ps_action_sp_strs))
self.main.parse('+'.join(self.misc_ps_sp_strs))
# ################# Sub-vocabulary definitions ########################
self.vis_main = self.main.create_subset(self.vis_sp_strs)
self.pos = self.main.create_subset(self.pos_sp_strs)
self.item = self.main.create_subset(self.num_sp_strs)
self.ps_task = self.main.create_subset(self.ps_task_sp_strs)
self.ps_state = self.main.create_subset(self.ps_state_sp_strs)
self.ps_dec = self.main.create_subset(self.ps_dec_sp_strs)
self.ps_cmp = self.main.create_subset(self.misc_ps_sp_strs)
self.ps_action = self.main.create_subset(self.ps_action_sp_strs)
self.ps_action_learn = \
self.main.create_subset(self.ps_action_learn_sp_strs)
self.reward = self.main.create_subset(self.reward_sp_strs)
# ############ Enumerated vocabulary definitions ######################
# --- Enumerated vocabulary, enumerates all possible combinations of
# position and item vectors (for debug purposes)
self.enum = Vocabulary(self.sp_dim, rng=rng)
for pos in self.pos_sp_strs:
for num in self.num_sp_strs:
sp_str = '%s*%s' % (pos, num)
self.enum.add(sp_str, self.main.parse(sp_str))
self.pos1 = Vocabulary(self.sp_dim, rng=rng)
for num in self.num_sp_strs:
sp_str = '%s*%s' % (self.pos_sp_strs[0], num)
self.pos1.add(sp_str, self.main.parse(sp_str))
def initialize_mtr_vocab(self, mtr_dim, mtr_sps):
self.mtr_dim = mtr_dim
self.mtr = Vocabulary(self.mtr_dim)
for i, sp_str in enumerate(self.num_sp_strs):
self.mtr.add(sp_str, mtr_sps[i, :])
self.mtr_unk = Vocabulary(self.mtr_dim)
self.mtr_unk.add(self.mtr_sp_strs[0], mtr_sps[-1, :])
self.mtr_disp = self.mtr.create_subset(self.num_sp_strs)
self.mtr_disp.readonly = False
# Disable read-only flag for display vocab so that things can be added
self.mtr_disp.add(self.mtr_sp_strs[0],
self.mtr_unk[self.mtr_sp_strs[0]].v)
def initialize_vis_vocab(self, vis_dim, vis_sps):
self.vis_dim = vis_dim
self.vis = Vocabulary(self.vis_dim)
for i, sp_str in enumerate(self.vis_sp_strs):
self.vis.add(sp_str, vis_sps[i, :])
def test_am_complex(Simulator, plt, seed, rng):
"""Complex auto-associative memory test.
Has a default output vector, outputs utilities, and becomes inhibited.
"""
D = 64
vocab = Vocabulary(D, rng=rng)
vocab.parse('A+B+C+D+E+F')
vocab2 = vocab.create_subset(["A", "B", "C", "D"])
def input_func(t):
if t < 0.25:
return vocab.parse('A+0.8*B').v
elif t < 0.5:
return vocab.parse('0.8*A+B').v
else:
return vocab.parse('E').v
def inhib_func(t):
return int(t > 0.75)
with nengo.Network('model', seed=seed) as m:
am = AssociativeMemory(vocab2,
default_output_vector=vocab.parse("F").v,
inhibitable=True,
output_utilities=True,
output_thresholded_utilities=True)
in_node = nengo.Node(output=input_func, label='input')
inhib_node = nengo.Node(output=inhib_func, label='inhib')
nengo.Connection(in_node, am.input)
nengo.Connection(inhib_node, am.inhibit)
in_p = nengo.Probe(in_node)
out_p = nengo.Probe(am.output, synapse=0.03)
utils_p = nengo.Probe(am.utilities, synapse=0.05)
utils_th_p = nengo.Probe(am.thresholded_utilities, synapse=0.05)
sim = Simulator(m)
sim.run(1.0)
t = sim.trange()
# Input: A+0.8B
more_a = (t >= 0.2) & (t < 0.25)
# Input: 0.8B+A
more_b = (t >= 0.45) & (t < 0.5)
# Input: E (but E isn't in the memory vocabulary, so should output F)
all_e = (t >= 0.7) & (t < 0.75)
# Input: E (but inhibited, so should output nothing)
inhib = (t >= 0.95)
def plot(i, y, ylabel):
plt.subplot(4, 1, i)
plt.plot(t, y)
plt.axvline(0.25, c='k')
plt.axvline(0.5, c='k')
plt.axvline(0.75, c='k')
plt.ylabel(ylabel)
plt.legend(vocab.keys[:y.shape[1]], loc='best', fontsize='xx-small')
plot(1, nengo.spa.similarity(sim.data[in_p], vocab), "Input")
plot(2, sim.data[utils_p], "Utilities")
plot(3, sim.data[utils_th_p], "Thresholded utilities")
plot(4, nengo.spa.similarity(sim.data[out_p], vocab), "Output")
assert all(np.mean(sim.data[utils_p][more_a], axis=0)[:2] > [0.8, 0.5])
assert all(np.mean(sim.data[utils_p][more_a], axis=0)[2:] < [0.01, 0.01])
assert all(np.mean(sim.data[utils_p][more_b], axis=0)[:2] > [0.5, 0.8])
assert all(np.mean(sim.data[utils_p][more_b], axis=0)[2:] < [0.01, 0.01])
assert similarity(sim.data[utils_p][all_e], np.ones((1, 4))) < 0.05
assert similarity(sim.data[utils_p][inhib], np.ones((1, 4))) < 0.05
assert all(np.mean(sim.data[utils_th_p][more_a], axis=0)[:2] > [0.8, 0.8])
assert all(
np.mean(sim.data[utils_th_p][more_a], axis=0)[2:] < [0.01, 0.01])
assert all(np.mean(sim.data[utils_th_p][more_b], axis=0)[:2] > [0.8, 0.8])
assert all(
np.mean(sim.data[utils_th_p][more_b], axis=0)[2:] < [0.01, 0.01])
assert similarity(sim.data[utils_th_p][all_e], np.ones((1, 4))) < 0.05
assert similarity(sim.data[utils_th_p][inhib], np.ones((1, 4))) < 0.05
assert similarity(sim.data[out_p][more_a], vocab.parse("A").v) > 0.8
assert similarity(sim.data[out_p][more_a], vocab.parse("B").v) > 0.8
assert similarity(sim.data[out_p][more_b], vocab.parse("A").v) > 0.8
assert similarity(sim.data[out_p][more_b], vocab.parse("B").v) > 0.8
assert similarity(sim.data[out_p][all_e], vocab.parse("F").v) > 0.8
assert similarity(sim.data[out_p][inhib], np.ones((1, D))) < 0.05
def make_mtr_sp(path_x, path_y):
path_x = convert_func_2_diff_func(path_x)
path_y = convert_func_2_diff_func(path_y)
return np.concatenate((path_x, path_y))
mtr_vocab = Vocabulary(cfg.mtr_dim, rng=cfg.rng)
for i, sp_str in enumerate(num_sp_strs):
mtr_sp_vec = make_mtr_sp(mtr_canon_paths_x[i, :], mtr_canon_paths_y[i, :])
mtr_vocab.add(sp_str, mtr_sp_vec)
mtr_unk_vocab = Vocabulary(cfg.mtr_dim, rng=cfg.rng)
mtr_unk_vocab.add(mtr_sp_strs[0], make_mtr_sp(mtr_canon_paths_x[-1, :],
mtr_canon_paths_y[-1, :]))
mtr_disp_vocab = mtr_vocab.create_subset(num_sp_strs)
mtr_disp_vocab.readonly = False # Disable read-only flag for display vocab
mtr_disp_vocab.add(mtr_sp_strs[0], mtr_unk_vocab[mtr_sp_strs[0]].v)
mtr_sp_scale_factor = float(mtr_canon_paths['size_scaling_factor'])
# ##################### Sub-vocabulary definitions ############################
vis_vocab = vocab.create_subset(vis_sp_strs)
vis_vocab_nums_inds = range(len(num_sp_strs))
vis_vocab_syms_inds = range(len(num_sp_strs), len(vis_sp_strs))
pos_vocab = vocab.create_subset(pos_sp_strs)
item_vocab = vocab.create_subset(num_sp_strs)
ps_task_vocab = vocab.create_subset(ps_task_sp_strs)
def test_am_complex(Simulator, plt, seed, rng):
"""Complex auto-associative memory test.
Has a default output vector, outputs utilities, and becomes inhibited.
"""
D = 64
vocab = Vocabulary(D, rng=rng)
vocab.parse('A+B+C+D+E+F')
vocab2 = vocab.create_subset(["A", "B", "C", "D"])
def input_func(t):
if t < 0.25:
return vocab.parse('A+0.8*B').v
elif t < 0.5:
return vocab.parse('0.8*A+B').v
else:
return vocab.parse('E').v
def inhib_func(t):
return int(t > 0.75)
with nengo.Network('model', seed=seed) as m:
am = AssociativeMemory(vocab2,
default_output_vector=vocab.parse("F").v,
inhibitable=True,
output_utilities=True,
output_thresholded_utilities=True)
in_node = nengo.Node(output=input_func, label='input')
inhib_node = nengo.Node(output=inhib_func, label='inhib')
nengo.Connection(in_node, am.input)
nengo.Connection(inhib_node, am.inhibit)
in_p = nengo.Probe(in_node)
out_p = nengo.Probe(am.output, synapse=0.03)
utils_p = nengo.Probe(am.utilities, synapse=0.05)
utils_th_p = nengo.Probe(am.thresholded_utilities, synapse=0.05)
sim = Simulator(m)
sim.run(1.0)
t = sim.trange()
# Input: A+0.8B
more_a = (t >= 0.2) & (t < 0.25)
# Input: 0.8B+A
more_b = (t >= 0.45) & (t < 0.5)
# Input: E (but E isn't in the memory vocabulary, so should output F)
all_e = (t >= 0.7) & (t < 0.75)
# Input: E (but inhibited, so should output nothing)
inhib = (t >= 0.95)
def plot(i, y, ylabel):
plt.subplot(4, 1, i)
plt.plot(t, y)
plt.axvline(0.25, c='k')
plt.axvline(0.5, c='k')
plt.axvline(0.75, c='k')
plt.ylabel(ylabel)
plt.legend(vocab.keys[:y.shape[1]], loc='best', fontsize='xx-small')
plot(1, nengo.spa.similarity(sim.data[in_p], vocab), "Input")
plot(2, sim.data[utils_p], "Utilities")
plot(3, sim.data[utils_th_p], "Thresholded utilities")
plot(4, nengo.spa.similarity(sim.data[out_p], vocab), "Output")
assert all(np.mean(sim.data[utils_p][more_a], axis=0)[:2] > [0.8, 0.5])
assert all(np.mean(sim.data[utils_p][more_a], axis=0)[2:] < [0.01, 0.01])
assert all(np.mean(sim.data[utils_p][more_b], axis=0)[:2] > [0.5, 0.8])
assert all(np.mean(sim.data[utils_p][more_b], axis=0)[2:] < [0.01, 0.01])
assert similarity(sim.data[utils_p][all_e], np.ones((1, 4))) < 0.05
assert similarity(sim.data[utils_p][inhib], np.ones((1, 4))) < 0.05
assert all(np.mean(sim.data[utils_th_p][more_a], axis=0)[:2] > [0.8, 0.8])
assert all(
np.mean(sim.data[utils_th_p][more_a], axis=0)[2:] < [0.01, 0.01])
assert all(np.mean(sim.data[utils_th_p][more_b], axis=0)[:2] > [0.8, 0.8])
assert all(
np.mean(sim.data[utils_th_p][more_b], axis=0)[2:] < [0.01, 0.01])
assert similarity(sim.data[utils_th_p][all_e], np.ones((1, 4))) < 0.05
assert similarity(sim.data[utils_th_p][inhib], np.ones((1, 4))) < 0.05
assert similarity(sim.data[out_p][more_a], vocab.parse("A").v) > 0.8
assert similarity(sim.data[out_p][more_a], vocab.parse("B").v) > 0.8
assert similarity(sim.data[out_p][more_b], vocab.parse("A").v) > 0.8
assert similarity(sim.data[out_p][more_b], vocab.parse("B").v) > 0.8
assert similarity(sim.data[out_p][all_e], vocab.parse("F").v) > 0.8
assert similarity(sim.data[out_p][inhib], np.ones((1, D))) < 0.05
class SpaunVocabulary(object):
def __init__(self):
self.main = None
self.sp_dim = 512
self.mtr_dim = 50
self.vis_dim = 200
# ############ Semantic pointer (strings) definitions #################
# --- Numerical semantic pointers ---
self.num_sp_strs = ['ZER', 'ONE', 'TWO', 'THR', 'FOR',
'FIV', 'SIX', 'SEV', 'EIG', 'NIN']
self.n_num_sp = len(self.num_sp_strs)
# --- Task semantic pointer list ---
# W - Drawing (Copying visual input)
# R - Recognition
# L - Learning (Bandit Task)
# M - Memory (forward serial recall)
# C - Counting
# A - Answering
# V - Rapid Variable Creation
# F - Fluid Induction (Ravens)
# X - Task precursor
# DEC - Decoding task (output to motor system)
self.ps_task_sp_strs = ['W', 'R', 'L', 'M', 'C', 'A', 'V', 'F', 'X',
'DEC', 'REACT', 'INSTR', 'CMP']
self.ps_task_vis_sp_strs = ['A', 'C', 'F', 'K', 'L', 'M', 'P', 'R',
'V', 'W']
# --- Task visual semantic pointer usage ---
# A - Task initialization
# F - Forward recall
# R - Reverse recall
# K - Q&A 'kind' probe
# P - Q&A 'position' probe
# --- Production system semantic pointers ---
# DECW - Decoding state (output to motor system, but for drawing task)
# DECI - Decoding state (output to motor system, but for inductn tasks)
self.ps_state_sp_strs = ['QAP', 'QAK', 'TRANS0', 'TRANS1', 'TRANS2',
'CNT0', 'CNT1', 'LEARN', 'DIRECT', 'INSTRP',
'INSTRV', 'TRANSC']
self.ps_dec_sp_strs = ['FWD', 'REV', 'CNT', 'DECW', 'DECI', 'NONE']
# --- Misc actions semantic pointers
self.ps_action_sp_strs = None
self.min_num_ps_actions = 3
# --- Misc visual semantic pointers ---
self.misc_vis_sp_strs = ['OPEN', 'CLOSE', 'SPACE', 'QM']
# --- Misc state semantic pointers ---
self.misc_ps_sp_strs = ['NO_MATCH', 'MATCH']
# --- 'I don't know' motor response vector
self.mtr_sp_strs = ['UNK']
# --- List of all visual semantic pointers ---
# self.vis_sp_strs = list(self.num_sp_strs)
# self.vis_sp_strs.extend(self.misc_vis_sp_strs)
# self.vis_sp_strs.extend(self.ps_task_vis_sp_strs)
# --- Position (enumerated) semantic pointers ---
self.pos_sp_strs = None
self.max_enum_list_pos = 8
# --- Operations semantic pointers
self.ops_sp_strs = ['ADD', 'INC']
# --- Reward semantic pointers
self.reward_n_sp_str = self.num_sp_strs[0]
self.reward_y_sp_str = self.num_sp_strs[1]
self.reward_sp_strs = [self.reward_n_sp_str, self.reward_y_sp_str]
# --- Instruction processing input and output tags
self.instr_tag_strs = ['VIS', 'TASK', 'STATE', 'DEC', 'DATA', 'ENABLE']
def write_header(self):
logger.write('# Spaun Vocabulary Options:\n')
logger.write('# -------------------------\n')
for param_name in sorted(self.__dict__.keys()):
param_value = getattr(self, param_name)
if not callable(param_value) and not isinstance(param_value, list)\
and not isinstance(param_value, Vocabulary) \
and not isinstance(param_value, SemanticPointer) \
and not isinstance(param_value, np.ndarray):
logger.write('# - %s = %s\n' % (param_name, param_value))
logger.write('\n')
def initialize(self, stim_SP_labels, num_learn_actions=3, rng=0):
if rng == 0:
rng = np.random.RandomState(int(time.time()))
# ############### Semantic pointer list definitions ###################
# --- Position (enumerated) semantic pointers ---
self.pos_sp_strs = ['POS%i' % (i + 1)
for i in range(self.max_enum_list_pos)]
# --- Unitary semantic pointers
self.unitary_sp_strs = [self.num_sp_strs[0], self.pos_sp_strs[0]]
self.unitary_sp_strs.extend(self.ops_sp_strs)
# --- Production system (action) semantic pointers ---
self.ps_action_learn_sp_strs = ['A%d' % (i + 1) for i in
range(num_learn_actions)]
self.ps_action_misc_sp_strs = []
self.ps_action_sp_strs = (self.ps_action_learn_sp_strs +
self.ps_action_misc_sp_strs)
# #################### Vocabulary definitions #########################
# --- Primary vocabulary ---
self.main = Vocabulary(self.sp_dim, unitary=self.unitary_sp_strs,
max_similarity=0.2, rng=rng)
# --- Add in visual sp's ---
self.main.parse('+'.join(self.misc_vis_sp_strs))
self.main.parse('+'.join(self.ps_task_vis_sp_strs))
for sp_str in list(stim_SP_labels):
if sp_str not in self.num_sp_strs and \
sp_str not in self.pos_sp_strs:
self.main.parse(sp_str)
# --- Add numerical sp's ---
self.main.parse('%s+%s' % (self.ops_sp_strs[0], self.num_sp_strs[0]))
add_sp = self.main[self.ops_sp_strs[0]]
num_sp = self.main[self.num_sp_strs[0]].copy()
for i in range(len(self.num_sp_strs) - 1):
num_sp = num_sp.copy() * add_sp
self.main.add(self.num_sp_strs[i + 1], num_sp)
self.add_sp = add_sp
# --- Add positional sp's ---
self.main.parse('%s+%s' % (self.ops_sp_strs[1], self.pos_sp_strs[0]))
inc_sp = self.main[self.ops_sp_strs[1]]
pos_sp = self.main[self.pos_sp_strs[0]].copy()
for i in range(len(self.pos_sp_strs) - 1):
pos_sp = pos_sp.copy() * inc_sp
self.main.add(self.pos_sp_strs[i + 1], pos_sp)
self.inc_sp = inc_sp
# --- Add production system sp's ---
self.main.parse('+'.join(self.ps_task_sp_strs))
self.main.parse('+'.join(self.ps_state_sp_strs))
self.main.parse('+'.join(self.ps_dec_sp_strs))
if len(self.ps_action_sp_strs) > 0:
self.main.parse('+'.join(self.ps_action_sp_strs))
self.main.parse('+'.join(self.misc_ps_sp_strs))
# --- Add instruction processing system sp's ---
self.main.parse('+'.join(self.instr_tag_strs))
# ################### Visual Vocabulary definitions ###################
self.vis_sp_strs = list(stim_SP_labels)
# Visual sp str vocab check
if (not all(x in self.vis_sp_strs for x in self.num_sp_strs)):
raise RuntimeError("Vocabulator - Stimulus vocabulary does not " +
"contain necessary Spaun NUM semantic pointer" +
" definitions.")
if (not all(x in self.vis_sp_strs for x in self.misc_vis_sp_strs)):
raise RuntimeError("Vocabulator - Stimulus vocabulary does not " +
"contain necessary Spaun MISC semantic " +
"pointer definitions.")
if (not all(x in self.vis_sp_strs for x in self.ps_task_vis_sp_strs)):
raise RuntimeError("Vocabulator - Stimulus vocabulary does not " +
"contain necessary Spaun PS semantic " +
"pointer definitions.")
# ################# Sub-vocabulary definitions ########################
self.vis_main = self.main.create_subset(self.vis_sp_strs)
self.pos = self.main.create_subset(self.pos_sp_strs)
self.item = self.main.create_subset(self.num_sp_strs)
self.item_1_index = self.main.create_subset(self.num_sp_strs[1:])
self.ps_task = self.main.create_subset(self.ps_task_sp_strs)
self.ps_state = self.main.create_subset(self.ps_state_sp_strs)
self.ps_dec = self.main.create_subset(self.ps_dec_sp_strs)
self.ps_cmp = self.main.create_subset(self.misc_ps_sp_strs)
self.ps_action = self.main.create_subset(self.ps_action_sp_strs)
self.ps_action_learn = \
self.main.create_subset(self.ps_action_learn_sp_strs)
self.reward = self.main.create_subset(self.reward_sp_strs)
self.instr = self.main.create_subset(self.instr_tag_strs)
# ############ Enumerated vocabulary definitions ######################
# --- Enumerated vocabulary, enumerates all possible combinations of
# position and item vectors (for debug purposes)
self.enum = Vocabulary(self.sp_dim, rng=rng)
for pos in self.pos_sp_strs:
for num in self.num_sp_strs:
sp_str = '%s*%s' % (pos, num)
self.enum.add(sp_str, self.main.parse(sp_str))
self.pos1 = Vocabulary(self.sp_dim, rng=rng)
for num in self.num_sp_strs:
sp_str = '%s*%s' % (self.pos_sp_strs[0], num)
self.pos1.add(sp_str, self.main.parse(sp_str))
# ############ Instruction vocabulary definitions #####################
# --- ANTECEDENT and CONSEQUENCE permutation transforms
self.perm_ant = np.arange(self.sp_dim)
self.perm_con = np.arange(self.sp_dim)
np.random.shuffle(self.perm_ant)
np.random.shuffle(self.perm_con)
self.perm_ant_inv = np.argsort(self.perm_ant)
self.perm_con_inv = np.argsort(self.perm_con)
def initialize_mtr_vocab(self, mtr_dim, mtr_sps):
self.mtr_dim = mtr_dim
self.mtr = Vocabulary(self.mtr_dim)
for i, sp_str in enumerate(self.num_sp_strs):
self.mtr.add(sp_str, mtr_sps[i, :])
self.mtr_unk = Vocabulary(self.mtr_dim)
self.mtr_unk.add(self.mtr_sp_strs[0], mtr_sps[-1, :])
self.mtr_disp = self.mtr.create_subset(self.num_sp_strs)
self.mtr_disp.readonly = False
# Disable read-only flag for display vocab so that things can be added
self.mtr_disp.add(self.mtr_sp_strs[0],
self.mtr_unk[self.mtr_sp_strs[0]].v)
def initialize_vis_vocab(self, vis_dim, vis_sps):
if vis_sps.shape[0] != len(self.vis_sp_strs):
raise RuntimeError('Vocabulatory.initialize_vis_vocab: ' +
'Mismatch in shape of raw vision SPs and ' +
'number of vision SP labels.')
self.vis_dim = vis_dim
self.vis = Vocabulary(self.vis_dim)
for i, sp_str in enumerate(self.vis_sp_strs):
self.vis.add(sp_str, vis_sps[i, :])
def parse_instr_sps_list(self, instr_sps_list):
instr_sps_list_sp = self.main.parse('0')
if len(instr_sps_list) > self.max_enum_list_pos:
raise ValueError('Vocabulator: Too many sequential ' +
'instructions. Max: %d' %
self.max_enum_list_pos + ', Got: ' +
len(instr_sps_list))
for i, instr_sps in enumerate(instr_sps_list):
instr_sps_list_sp += (self.main.parse('POS%i' % (i + 1)) *
self.parse_instr_sps(*instr_sps))
return instr_sps_list_sp
def parse_instr_sps(self, ant_sp='0', cons_sp='0'):
# Note: The ant and con permutations are used here to separate the
# possible POS tags in the ant/con from the instruction POS tag.
# This permutation is not necessary if the instruction POS tags
# differ from the number-representation POS tags.
return SemanticPointer(self.main.parse(ant_sp).v[self.perm_ant] +
self.main.parse(cons_sp).v[self.perm_con])
def test_am_default_output_inhibit_utilities(Simulator):
"""Auto-associative memory (non-wta) complex test.
Options: defaults to predefined vector if no match is found,
threshold = 0.3, inhibitable, non-wta, outputs utilities and thresholded
utilities.
"""
rng = np.random.RandomState(1)
D = 64
vocab = Vocabulary(D, rng=rng)
vocab.parse('A+B+C+D+E+F')
vocab2 = vocab.create_subset(["A", "B", "C", "D"])
def input_func(t):
if t < 0.25:
return vocab.parse('A+0.8*B').v
elif t < 0.5:
return vocab.parse('0.8*A+B').v
else:
return vocab.parse('E').v
def inhib_func(t):
return int(t > 0.75)
m = nengo.Network('model', seed=123)
with m:
am = AssociativeMemory(vocab2,
default_output_vector=vocab.parse("F").v,
inhibitable=True, output_utilities=True,
output_thresholded_utilities=True)
in_node = nengo.Node(output=input_func, label='input')
inhib_node = nengo.Node(output=inhib_func, label='inhib')
out_node = nengo.Node(size_in=D, label='output')
utils_node = nengo.Node(size_in=4, label='utils')
utils_th_node = nengo.Node(size_in=4, label='utils_th')
nengo.Connection(in_node, am.input)
nengo.Connection(inhib_node, am.inhibit)
nengo.Connection(am.output, out_node, synapse=0.03)
nengo.Connection(am.utilities, utils_node, synapse=0.05)
nengo.Connection(am.thresholded_utilities, utils_th_node, synapse=0.05)
in_p = nengo.Probe(in_node)
out_p = nengo.Probe(out_node)
utils_p = nengo.Probe(utils_node)
utils_th_p = nengo.Probe(utils_th_node)
sim = Simulator(m)
sim.run(1.0)
assert np.allclose(sim.data[in_p][240:250], vocab.parse("A+0.8*B").v,
atol=.1, rtol=.01)
assert np.allclose(sim.data[in_p][490:500], vocab.parse("0.8*A+B").v,
atol=.1, rtol=.01)
assert np.allclose(sim.data[in_p][-10:], vocab.parse("E").v,
atol=.1, rtol=.01)
assert np.allclose(sim.data[out_p][240:250], vocab.parse("A+B").v,
atol=.2, rtol=.05)
assert np.allclose(sim.data[out_p][490:500], vocab.parse("A+B").v,
atol=.1, rtol=.01)
assert np.allclose(sim.data[out_p][740:750], vocab.parse("F").v,
atol=.1, rtol=.01)
assert np.allclose(sim.data[out_p][-10:], vocab.parse("0").v,
atol=.1, rtol=.01)
assert np.allclose(sim.data[utils_p][240:250], [1, 0.75, 0, 0],
atol=.1, rtol=.01)
assert np.allclose(sim.data[utils_p][490:500], [0.75, 1, 0, 0],
atol=.1, rtol=.01)
assert np.allclose(sim.data[utils_p][740:750], [0, 0, 0, 0],
atol=.1, rtol=.01)
assert np.allclose(sim.data[utils_p][-10:], [0, 0, 0, 0],
atol=.1, rtol=.01)
assert np.allclose(sim.data[utils_th_p][240:250], [1.05, 1.05, 0, 0],
atol=.2, rtol=.05)
assert np.allclose(sim.data[utils_th_p][490:500], [1.05, 1.05, 0, 0],
atol=.1, rtol=.05)
assert np.allclose(sim.data[utils_th_p][740:750], [0, 0, 0, 0],
atol=.1, rtol=.01)
assert np.allclose(sim.data[utils_th_p][-10:], [0, 0, 0, 0],
atol=.1, rtol=.01)
def test_am_default_output_inhibit_utilities(Simulator):
"""Auto-associative memory (non-wta) complex test.
Options: defaults to predefined vector if no match is found,
threshold = 0.3, inhibitable, non-wta, outputs utilities and thresholded
utilities.
"""
rng = np.random.RandomState(1)
D = 64
vocab = Vocabulary(D, rng=rng)
vocab.parse('A+B+C+D+E+F')
vocab2 = vocab.create_subset(["A", "B", "C", "D"])
def input_func(t):
if t < 0.25:
return vocab.parse('A+0.8*B').v
elif t < 0.5:
return vocab.parse('0.8*A+B').v
else:
return vocab.parse('E').v
def inhib_func(t):
return int(t > 0.75)
m = nengo.Network('model', seed=123)
with m:
am = AssociativeMemory(vocab2,
default_output_vector=vocab.parse("F").v,
inhibitable=True,
output_utilities=True,
output_thresholded_utilities=True)
in_node = nengo.Node(output=input_func, label='input')
inhib_node = nengo.Node(output=inhib_func, label='inhib')
out_node = nengo.Node(size_in=D, label='output')
utils_node = nengo.Node(size_in=4, label='utils')
utils_th_node = nengo.Node(size_in=4, label='utils_th')
nengo.Connection(in_node, am.input)
nengo.Connection(inhib_node, am.inhibit)
nengo.Connection(am.output, out_node, synapse=0.03)
nengo.Connection(am.utilities, utils_node, synapse=0.05)
nengo.Connection(am.thresholded_utilities, utils_th_node, synapse=0.05)
in_p = nengo.Probe(in_node)
out_p = nengo.Probe(out_node)
utils_p = nengo.Probe(utils_node)
utils_th_p = nengo.Probe(utils_th_node)
sim = Simulator(m)
sim.run(1.0)
assert np.allclose(sim.data[in_p][240:250],
vocab.parse("A+0.8*B").v,
atol=.1,
rtol=.01)
assert np.allclose(sim.data[in_p][490:500],
vocab.parse("0.8*A+B").v,
atol=.1,
rtol=.01)
assert np.allclose(sim.data[in_p][-10:],
vocab.parse("E").v,
atol=.1,
rtol=.01)
assert np.allclose(sim.data[out_p][240:250],
vocab.parse("A+B").v,
atol=.2,
rtol=.05)
assert np.allclose(sim.data[out_p][490:500],
vocab.parse("A+B").v,
atol=.1,
rtol=.01)
assert np.allclose(sim.data[out_p][740:750],
vocab.parse("F").v,
atol=.1,
rtol=.01)
assert np.allclose(sim.data[out_p][-10:],
vocab.parse("0").v,
atol=.1,
rtol=.01)
assert np.allclose(sim.data[utils_p][240:250], [1, 0.75, 0, 0],
atol=.1,
rtol=.01)
assert np.allclose(sim.data[utils_p][490:500], [0.75, 1, 0, 0],
atol=.1,
rtol=.01)
assert np.allclose(sim.data[utils_p][740:750], [0, 0, 0, 0],
atol=.1,
rtol=.01)
assert np.allclose(sim.data[utils_p][-10:], [0, 0, 0, 0],
atol=.1,
rtol=.01)
assert np.allclose(sim.data[utils_th_p][240:250], [1.05, 1.05, 0, 0],
atol=.2,
rtol=.05)
assert np.allclose(sim.data[utils_th_p][490:500], [1.05, 1.05, 0, 0],
atol=.1,
rtol=.05)
assert np.allclose(sim.data[utils_th_p][740:750], [0, 0, 0, 0],
atol=.1,
rtol=.01)
assert np.allclose(sim.data[utils_th_p][-10:], [0, 0, 0, 0],
atol=.1,
rtol=.01)
