def templateIncorrectPatternForSimulation(pattern, ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore=ccore_flag)
try:
net.simulate(10, 10, pattern)
except:
return
assert False
def templateIncorrectPatternForTraining(patterns, ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag)
try:
net.train(patterns)
except:
return
assert False
def templatePatternVisualizer(self, collect_dynamic, ccore_flag=False):
net = syncpr(5, 0.1, 0.1, ccore=ccore_flag)
output_dynamic = net.simulate(10, 10, [-1, 1, -1, 1, -1],
solve_type.RK4, collect_dynamic)
syncpr_visualizer.show_pattern(output_dynamic, 5, 2)
syncpr_visualizer.animate_pattern_recognition(output_dynamic, 1, 5)
def templateTrainNetworkAndRecognizePattern(self, ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag)
patterns = []
patterns += [[1, 1, 1, 1, 1, -1, -1, -1, -1, -1]]
patterns += [[-1, -1, -1, -1, -1, 1, 1, 1, 1, 1]]
net.train(patterns)
# recognize it
for i in range(len(patterns)):
output_dynamic = net.simulate(10, 10, patterns[i], solve_type.RK4,
True)
ensembles = output_dynamic.allocate_sync_ensembles(0.5)
assert len(ensembles) == 2
assert len(ensembles[0]) == len(ensembles[1])
# sort results
ensembles[0].sort()
ensembles[1].sort()
assert (ensembles[0] == [0, 1, 2, 3, 4]) or (ensembles[0]
== [5, 6, 7, 8, 9])
assert (ensembles[1] == [0, 1, 2, 3, 4]) or (ensembles[1]
== [5, 6, 7, 8, 9])
def testOutputDinamicLengthSimulation(self):
net = syncpr(5, 0.1, 0.1, ccore=False)
output_dynamic = net.simulate(10,
10, [-1, 1, -1, 1, -1],
solution=solve_type.FAST,
collect_dynamic=True)
assert len(output_dynamic) == 11
def templateOutputDynamicLengthDynamicSimulation(collect_flag, ccore_flag):
net = syncpr(5, 0.1, 0.1, ccore_flag);
output_dynamic = net.simulate_dynamic([-1, 1, -1, 1, -1], solution = solve_type.FAST, collect_dynamic = collect_flag);
if (collect_flag is True):
assert len(output_dynamic) > 1;
else:
assert len(output_dynamic) == 1;
def templateOutputDynamicLengthStaticSimulation(collect_flag, ccore_flag):
net = syncpr(5, 0.1, 0.1, ccore_flag);
output_dynamic = net.simulate_static(10, 10, [-1, 1, -1, 1, -1], solution = solve_type.FAST, collect_dynamic = collect_flag);
if (collect_flag is True):
assert len(output_dynamic) == 11; # 10 steps without initial values.
else:
assert len(output_dynamic) == 1;
def templateOutputDynamicLengthDynamicSimulation(collect_flag, ccore_flag):
net = syncpr(5, 0.1, 0.1, ccore_flag);
output_dynamic = net.simulate_dynamic([-1, 1, -1, 1, -1], solution = solve_type.FAST, collect_dynamic = collect_flag);
if (collect_flag is True):
assert len(output_dynamic) > 1;
else:
assert len(output_dynamic) == 1;
def templateOutputDynamicLengthStaticSimulation(collect_flag, ccore_flag):
net = syncpr(5, 0.1, 0.1, ccore_flag);
output_dynamic = net.simulate_static(10, 10, [-1, 1, -1, 1, -1], solution = solve_type.FAST, collect_dynamic = collect_flag);
if (collect_flag is True):
assert len(output_dynamic) == 11; # 10 steps without initial values.
else:
assert len(output_dynamic) == 1;
def templateStaticSimulation(self, ccore_falg):
net = syncpr(10, 0.1, 0.1, ccore_falg)
patterns = []
patterns += [[1, 1, 1, 1, 1, -1, -1, -1, -1, -1]]
patterns += [[-1, -1, -1, -1, -1, 1, 1, 1, 1, 1]]
net.train(patterns)
net.simulate_static(20, 10, patterns[0], solve_type.RK4)
memory_order = net.memory_order(patterns[0])
assert (memory_order > 0.95) and (memory_order <= 1.000005)
def templateDynamicSimulation(self, ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag)
patterns = []
patterns += [[1, 1, 1, 1, 1, -1, -1, -1, -1, -1]]
patterns += [[-1, -1, -1, -1, -1, 1, 1, 1, 1, 1]]
net.train(patterns)
net.simulate_dynamic(patterns[0], order=0.998, solution=solve_type.RK4)
memory_order = net.memory_order(patterns[0])
assert (memory_order > 0.998) and (memory_order <= 1.0)
def templateStaticSimulation(ccore_falg):
net = syncpr(10, 0.1, 0.1, ccore_falg);
patterns = [];
patterns += [ [1, 1, 1, 1, 1, -1, -1, -1, -1, -1] ];
patterns += [ [-1, -1, -1, -1, -1, 1, 1, 1, 1, 1] ];
net.train(patterns);
net.simulate_static(20, 10, patterns[0], solve_type.RK4);
memory_order = net.memory_order(patterns[0]);
assert (memory_order > 0.95) and (memory_order <= 1.000005);
def templateDynamicSimulation(ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag);
patterns = [];
patterns += [ [1, 1, 1, 1, 1, -1, -1, -1, -1, -1] ];
patterns += [ [-1, -1, -1, -1, -1, 1, 1, 1, 1, 1] ];
net.train(patterns);
net.simulate_dynamic(patterns[0], order = 0.998, solution = solve_type.RK4);
memory_order = net.memory_order(patterns[0]);
assert (memory_order > 0.998) and (memory_order <= 1.0);
def templateGlobalSyncOrder(self, ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag);
patterns = [ [1, 1, 1, 1, 1, -1, -1, -1, -1, -1] ];
patterns += [ [-1, -1, -1, -1, -1, 1, 1, 1, 1, 1] ];
global_sync_order = net.sync_order();
assert (global_sync_order < 1.0) and (global_sync_order > 0.0);
net.train(patterns);
global_sync_order = net.sync_order();
assert (global_sync_order < 1.0) and (global_sync_order > 0.0);
def templateLocalSyncOrder(ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag);
patterns = [ [1, 1, 1, 1, 1, -1, -1, -1, -1, -1] ];
patterns += [ [-1, -1, -1, -1, -1, 1, 1, 1, 1, 1] ];
local_sync_order = net.sync_local_order();
assert (local_sync_order < 1.0) and (local_sync_order > 0.0);
net.train(patterns);
local_sync_order = net.sync_local_order();
assert (local_sync_order < 1.0) and (local_sync_order > 0.0);
def templateLocalSyncOrder(self, ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag)
patterns = [[1, 1, 1, 1, 1, -1, -1, -1, -1, -1]]
patterns += [[-1, -1, -1, -1, -1, 1, 1, 1, 1, 1]]
local_sync_order = net.sync_local_order()
assert (local_sync_order < 1.0) and (local_sync_order > 0.0)
net.train(patterns)
local_sync_order = net.sync_local_order()
assert (local_sync_order < 1.0) and (local_sync_order > 0.0)
def templateGlobalSyncOrder(ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag)
patterns = [[1, 1, 1, 1, 1, -1, -1, -1, -1, -1]]
patterns += [[-1, -1, -1, -1, -1, 1, 1, 1, 1, 1]]
global_sync_order = net.sync_order()
assert (global_sync_order < 1.0) and (global_sync_order > 0.0)
net.train(patterns)
global_sync_order = net.sync_order()
assert (global_sync_order < 1.0) and (global_sync_order > 0.0)
def templateLocalSyncOrder(ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag);
patterns = [ [1, 1, 1, 1, 1, -1, -1, -1, -1, -1] ];
patterns += [ [-1, -1, -1, -1, -1, 1, 1, 1, 1, 1] ];
local_sync_order = net.sync_local_order();
assert (local_sync_order < 1.0) and (local_sync_order > 0.0);
net.train(patterns);
local_sync_order = net.sync_local_order();
assert (local_sync_order < 1.0) and (local_sync_order > 0.0);
def templateGlobalSyncOrder(self, ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag);
patterns = [ [1, 1, 1, 1, 1, -1, -1, -1, -1, -1] ];
patterns += [ [-1, -1, -1, -1, -1, 1, 1, 1, 1, 1] ];
global_sync_order = net.sync_order();
assert (global_sync_order < 1.0) and (global_sync_order > 0.0);
net.train(patterns);
global_sync_order = net.sync_order();
assert (global_sync_order < 1.0) and (global_sync_order > 0.0);
def templateMemoryOrder(ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag);
patterns = [];
patterns += [ [1, 1, 1, 1, 1, -1, -1, -1, -1, -1] ];
patterns += [ [-1, -1, -1, -1, -1, 1, 1, 1, 1, 1] ];
net.train(patterns);
assert net.memory_order(patterns[0]) < 0.8;
assert net.memory_order(patterns[1]) < 0.8;
for pattern in patterns:
net.simulate(20, 10, pattern, solve_type.RK4);
memory_order = net.memory_order(pattern);
assert (memory_order > 0.95) and (memory_order <= 1.000005);
def templateMemoryOrder(self, ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag)
patterns = []
patterns += [[1, 1, 1, 1, 1, -1, -1, -1, -1, -1]]
patterns += [[-1, -1, -1, -1, -1, 1, 1, 1, 1, 1]]
net.train(patterns)
assert net.memory_order(patterns[0]) < 0.8
assert net.memory_order(patterns[1]) < 0.8
for pattern in patterns:
net.simulate(20, 10, pattern, solve_type.RK4)
memory_order = net.memory_order(pattern)
assert (memory_order > 0.95) and (memory_order <= 1.000005)
def template_recognition_image(images, steps, time, corruption=0.1):
samples = []
for file_name in images:
data = read_image(file_name)
image_pattern = rgb2gray(data)
for index_pixel in range(len(image_pattern)):
if (image_pattern[index_pixel] < 128):
image_pattern[index_pixel] = 1
else:
image_pattern[index_pixel] = -1
samples += [image_pattern]
net = syncpr(len(samples[0]), 0.3, 0.3, ccore=True)
net.train(samples)
# Recognize the each learned pattern
for i in range(len(samples)):
sync_output_dynamic = net.simulate(steps, time, samples[i],
solve_type.RK4, True)
syncpr_visualizer.show_output_dynamic(sync_output_dynamic)
syncpr_visualizer.show_pattern(sync_output_dynamic, 10, 10)
# corrupt a little bit by black and white pixels
for _ in range(math.floor(len(samples[i]) * corruption)):
random.seed()
random_pixel = math.floor(random.random() * len(samples[i]))
samples[i][random_pixel] = 1
random_pixel = math.floor(random.random() * len(samples[i]))
samples[i][random_pixel] = -1
sync_output_dynamic = net.simulate(steps, time, samples[i],
solve_type.RK4, True)
syncpr_visualizer.show_output_dynamic(sync_output_dynamic)
syncpr_visualizer.show_pattern(sync_output_dynamic, 10, 10)
syncpr_visualizer.animate_pattern_recognition(
sync_output_dynamic, 10, 10, title="Pattern Recognition")
def template_recognition_image(images, steps, time, corruption = 0.1):
samples = [];
for file_name in images:
data = read_image(file_name);
image_pattern = rgb2gray(data);
for index_pixel in range(len(image_pattern)):
if (image_pattern[index_pixel] < 128):
image_pattern[index_pixel] = 1;
else:
image_pattern[index_pixel] = -1;
samples += [ image_pattern ];
net = syncpr(len(samples[0]), 0.3, 0.3, ccore = True);
net.train(samples);
# Recognize the each learned pattern
for i in range(len(samples)):
sync_output_dynamic = net.simulate(steps, time, samples[i], solve_type.RK4, True);
syncpr_visualizer.show_output_dynamic(sync_output_dynamic);
syncpr_visualizer.show_pattern(sync_output_dynamic, 10, 10);
# corrupt a little bit by black and white pixels
for _ in range( math.floor(len(samples[i]) * corruption) ):
random.seed();
random_pixel = math.floor(random.random() * len(samples[i]));
samples[i][random_pixel] = 1;
random_pixel = math.floor(random.random() * len(samples[i]));
samples[i][random_pixel] = -1;
sync_output_dynamic = net.simulate(steps, time, samples[i], solve_type.RK4, True);
syncpr_visualizer.show_output_dynamic(sync_output_dynamic);
syncpr_visualizer.show_pattern(sync_output_dynamic, 10, 10);
syncpr_visualizer.animate_pattern_recognition(sync_output_dynamic, 10, 10, title = "Pattern Recognition");
def templateTrainNetworkAndRecognizePattern(ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag);
patterns = [];
patterns += [ [1, 1, 1, 1, 1, -1, -1, -1, -1, -1] ];
patterns += [ [-1, -1, -1, -1, -1, 1, 1, 1, 1, 1] ];
net.train(patterns);
# recognize it
for i in range(len(patterns)):
output_dynamic = net.simulate(10, 10, patterns[i], solve_type.RK4, True);
ensembles = output_dynamic.allocate_sync_ensembles(0.5);
assert len(ensembles) == 2;
assert len(ensembles[0]) == len(ensembles[1]);
# sort results
ensembles[0].sort();
ensembles[1].sort();
assert (ensembles[0] == [0, 1, 2, 3, 4]) or (ensembles[0] == [5, 6, 7, 8, 9]);
assert (ensembles[1] == [0, 1, 2, 3, 4]) or (ensembles[1] == [5, 6, 7, 8, 9]);
def templateIncorrectPatternForTraining(self, patterns, ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag);
self.assertRaises(Exception, net.train, patterns);
def templateIncorrectPatternForSimulation(self, pattern):
net = syncpr(10, 0.1, 0.1);
self.assertRaises(Exception, net.simulate, 10, 10, pattern);
def testCreateTenOscillatorsNetworkByCore(self):
net = syncpr(10, 0.1, 0.1, True);
assert len(net) == 10;
def templatePatternVisualizer(collect_dynamic, ccore_flag = False):
net = syncpr(5, 0.1, 0.1, ccore = ccore_flag);
output_dynamic = net.simulate(10, 10, [-1, 1, -1, 1, -1], solve_type.RK4, collect_dynamic);
syncpr_visualizer.show_pattern(output_dynamic, 5, 2);
syncpr_visualizer.animate_pattern_recognition(output_dynamic, 1, 5);
def templateIncorrectPatternForTraining(patterns, ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag);
try: net.train(patterns);
except: return;
assert False;
def templateOutputDynamic(self, solver, ccore=False):
net = syncpr(5, 0.1, 0.1, ccore)
output_dynamic = net.simulate(10, 10, [-1, 1, -1, 1, -1], solver, True)
assert len(output_dynamic) == 11
def testCreateTenOscillatorsNetwork(self):
net = syncpr(10, 0.1, 0.1)
assert len(net) == 10
def testOutputDinamicLengthSimulation(self):
net = syncpr(5, 0.1, 0.1);
output_dynamic = net.simulate(10, 10, [-1, 1, -1, 1, -1], solution = solve_type.FAST, collect_dynamic = True);
assert len(output_dynamic) == 11; # 10 steps without initial values.
def templatePatternVisualizer(self, collect_dynamic):
net = syncpr(5, 0.1, 0.1);
output_dynamic = net.simulate(10, 10, [-1, 1, -1, 1, -1], solve_type.RK4, collect_dynamic);
# test that we don't have any exception during vizualization.
syncpr_visualizer.show_pattern(output_dynamic, 5, 2);
def testCreateTenOscillatorsNetwork(self):
net = syncpr(10, 0.1, 0.1);
assert len(net) == 10;
def testCreateTenOscillatorsNetwork(self):
net = syncpr(10, 0.1, 0.1, ccore=False);
assert len(net) == 10;
def templateOutputDynamic(solver, ccore):
net = syncpr(5, 0.1, 0.1, ccore);
output_dynamic = net.simulate(10, 10, [-1, 1, -1, 1, -1], solver, True);
assert len(output_dynamic) == 11; # 10 steps without initial values.
def testOutputDinamicLengthSimulation(self):
net = syncpr(5, 0.1, 0.1);
output_dynamic = net.simulate(10, 10, [-1, 1, -1, 1, -1], solution = solve_type.FAST, collect_dynamic = True);
assert len(output_dynamic) == 11; # 10 steps without initial values.
def testCreateHundredOscillatorsNetworkByCore(self):
net = syncpr(100, 0.1, 0.1, True);
assert len(net) == 100;
def testCreateHundredOscillatorsNetwork(self):
net = syncpr(100, 0.1, 0.1, ccore=False)
assert len(net) == 100
def templatePatternVisualizer(self, collect_dynamic):
net = syncpr(5, 0.1, 0.1);
output_dynamic = net.simulate(10, 10, [-1, 1, -1, 1, -1], solve_type.RK4, collect_dynamic);
# test that we don't have any exception during vizualization.
syncpr_visualizer.show_pattern(output_dynamic, 5, 2);
def templateIncorrectPatternForSimulation(pattern, ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore=ccore_flag);
try: net.simulate(10, 10, pattern);
except: return;
assert False;
def templateIncorrectPatternForSimulation(self, pattern):
net = syncpr(10, 0.1, 0.1)
self.assertRaises(Exception, net.simulate, 10, 10, pattern)
def testCreateHundredOscillatorsNetworkByCore(self):
net = syncpr(100, 0.1, 0.1, True)
assert len(net) == 100
def templateOutputDynamic(solver, ccore):
net = syncpr(5, 0.1, 0.1, ccore);
output_dynamic = net.simulate(10, 10, [-1, 1, -1, 1, -1], solver, True);
assert len(output_dynamic) == 11; # 10 steps without initial values.
def templateIncorrectPatternForTraining(self, patterns, ccore_flag):
net = syncpr(10, 0.1, 0.1, ccore_flag)
self.assertRaises(Exception, net.train, patterns)
def testCreateHundredOscillatorsNetwork(self):
net = syncpr(100, 0.1, 0.1, ccore=False);
assert len(net) == 100;