def main():
i_gen = InputGenerator(0, T, NUM_TIME_STEPS)
data = i_gen.generate_sin(amplitude=AMPLITUDE)
num_train = int(len(data) * RATIO_TRAIN)
train_data = data[:num_train]
model = ReservoirNetWork(inputs=train_data,
num_input_nodes=NUM_INPUT_NODES,
num_reservoir_nodes=NUM_RESERVOIR_NODES,
num_output_nodes=NUM_OUTPUT_NODES,
leak_rate=LEAK_RATE)
model.train() # 訓練
train_result = model.get_train_result() # 訓練の結果を取得
num_predict = int(len(data[num_train:]))
predict_result = model.predict(num_predict)
t = np.linspace(0, T, NUM_TIME_STEPS)
## plot
plt.plot(t, data, label="inputs")
plt.plot(t[:num_train], train_result, label="trained")
plt.plot(t[num_train:], predict_result, label="predicted")
plt.axvline(x=int(T * RATIO_TRAIN), label="end of train",
color="green") # border of train and prediction
plt.legend()
plt.title("Echo State Network Sin Prediction")
plt.xlabel("time[ms]")
plt.ylabel("y(t)")
plt.show()
print(model.test())
def main():
i_gen = InputGenerator(0, T, NUM_TIME_STEPS)
data = i_gen.generate_logistic()
num_train = int(len(data) * RATIO_TRAIN)
train_data = data[:num_train]
print("number of training is" + str(num_train))
print(dt)
model = ReservoirNetWork(inputs=train_data,
teacher=train_data,
num_input_nodes=NUM_INPUT_NODES,
num_reservoir_nodes=NUM_RESERVOIR_NODES,
num_output_nodes=NUM_OUTPUT_NODES,
leak_rate=LEAK_RATE)
model.offline_training() #オフライン学習で出力重みを初期化
model.online_training() #(オンライン学習で)重み更新
train_result = model.get_train_result() # 訓練の結果を取得
num_predict = int(len(data[num_train:]))
predict_result = model.predict(length_of_sequence=num_predict)
t = np.linspace(0, T, NUM_TIME_STEPS)
## plot
plt.plot(t, data, label="inputs")
plt.plot(t[:num_train], train_result, label="trained")
plt.plot(t[num_train:], predict_result, label="predicted")
plt.axvline(x=int(T * RATIO_TRAIN), label="end of train",
color="red") # border of train and prediction
plt.legend()
plt.title("Echo State Network Logistic Prediction" +
"//Number of Reservoir Nodes " + str(NUM_RESERVOIR_NODES))
plt.xlabel("time")
plt.ylabel("y(t)")
plt.show()
def __init__(self, file):
super(ProteinDataset, self).__init__()
pdb_identifies_file = file # '../inputs/train.txt' # ../inputs/pdb_identifiers.txt'
self.pdb_identifiers = Utility.get_pdb_identifiers(pdb_identifies_file)
print(len(self.pdb_identifiers), " proteins in hand")
self.input_generator = InputGenerator()
self.records = self.generate_input_output_sets()
def main():
i_gen = InputGenerator(0, T, NUM_TIME_STEPS)
data = i_gen.generate_lorentz()
num_train = int(len(data) * RATIO_TRAIN)
train_data = data[:num_train]
print("number of training is"+ str(num_train))
print(dt)
model = ReservoirNetWork(inputs=train_data,
teacher = train_data,
num_input_nodes=NUM_INPUT_NODES,
num_reservoir_nodes=NUM_RESERVOIR_NODES,
num_output_nodes=NUM_OUTPUT_NODES,
leak_rate=LEAK_RATE)
# model.offline_training() #オフライン学習で出力重みを初期化
model.online_training()#(オンライン学習で)重み更新
train_result = model.get_train_result() # 訓練の結果を取得
num_predict = int(len(data[num_train:]))
predict_result = model.predict(num_predict)
train_result = model.get_train_result() # 訓練の結果を取得
# print(train_result)
## plot
fig = plt.figure()
ax = Axes3D(fig)
# ax.plot(train_result[0,:], train_result[1,:], train_result[2,:])
plt.show()
def processParams(self, params):
# Directory for experience
directory = self.setupDir(params)
# Set up generator
generator = InputGenerator(
self.gt,
chunk_size=params["nb_timesteps"],
batch_size=params["batch_size"],
)
# Set up model
model = RNN(params)
model = model.model
# Set up loss
loss = CustomLoss(
lambda_roll=params["lambda_roll"],
lambda_pitch=params["lambda_pitch"],
lambda_yaw=params["lambda_yaw"],
lambda_thrust=params["lambda_thrust"],
loss_func=params["loss_func"],
)
decay = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=2,
verbose=1,
min_lr=1e-6)
# Set up loss
optimizer = Adam(
lr=params["lr"],
decay=params["decay"],
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
)
# Set up callbacks
mcp = ModelCheckpoint(
directory + "/model.{epoch:03d}.h5",
verbose=1,
save_weights_only=False,
)
lrp = PrintLR()
#lrs = LearningRateScheduler(step_decay)
callbacks = [mcp, lrp, decay]
# return all params
return generator, model, loss, optimizer, callbacks
def run_tests(self):
is_input_consistent_str = 'consistent'
if not self.is_input_consistent:
is_input_consistent_str = 'in' + is_input_consistent_str
print 'Start testing for %s input...\n' % is_input_consistent_str
for dimension in self.dimensions:
random_no_points_per_axis = np.random.randint(2, 12,
dimension).tolist()
no_points_per_axis = ' '.join(
[str(no) for no in random_no_points_per_axis])
no_points_per_axis_display = ', '.join(
[str(no) for no in random_no_points_per_axis])
print ' Testing for dimension %d with [%s] points on axis...' % \
(dimension, no_points_per_axis_display)
for epsilon in self.epsilons:
print ' Testing for epsilon: ', epsilon
for from_poly in [True, False]:
input_gen = InputGenerator(PATH_TO_FILE,
self.is_input_consistent,
dimension,
no_points_per_axis,
rand_points_axis=True,
from_poly=from_poly,
eps=epsilon)
input_gen.generate_test_file()
# Silence the print statements to stdout.
with Utils.nostdout():
cons = Consistency(PATH_TO_FILE, input_gen, False,
False, True)
result = cons.solve_LP_problem()
if result is not self.is_input_consistent:
raise RuntimeError(
'Counterexample found, aborting. See the file %s '
'for details.' % PATH_TO_FILE)
print '\n...Finished testing. No counterexamples found.\n'
def main():
(options, args) = command_line_arguments()
validate_options(options)
input_generator = None
if options.gen_cons_input or options.gen_incons_input:
input_generator = InputGenerator(options.input_file,
options.gen_cons_input is not None,
options.dimension,
options.no_points_per_axis,
options.rand_points_axis,
options.from_poly, options.epsilon)
input_generator.generate_test_file()
cons = Consistency(options.input_file, input_generator,
options.plot_surfaces, options.plot_rand_heights,
options.verbose)
# Run the LP algorithm to decide consistency.
cons.solve_LP_problem()
def main():
print(NUM_TIME_STEPS)
ing = InputGenerator(0,T,NUM_TIME_STEPS)
train_data = ing.generate_sin()
num_train = int(len(train_data) * RATIO_TRAIN)
print(train_data)
target_data = np.sign(train_data)
num_target = int(len(target_data) * RATIO_TRAIN)
print(target_data)
model = ReservoirNetWork(inputs=train_data, #ReservoirNetwork.pyを参照
outputs_target = target_data,
num_input_nodes=NUM_INPUT_NODES,
num_reservoir_nodes=NUM_RESERVOIR_NODES,
num_output_nodes=NUM_OUTPUT_NODES,
leak_rate=LEAK_RATE)
model.train() # 訓練
train_result = model.get_train_result() # 訓練の結果を取得
num_predict = int(len(train_data[num_target:]))
predict_result = model.predict(num_predict)
t = np.linspace(0,T,NUM_TIME_STEPS)
## plot
plt.plot(t, train_data, label="inputs")
plt.plot(t[:num_train], train_result[:num_train], label="trained")
plt.plot(t[num_target:], predict_result, label="predicted")
plt.axvline(x=int(T * RATIO_TRAIN), label="end of train", color="green") # border of train and prediction
plt.legend()
plt.title("Echo State Network TEST Prediction")
plt.xlabel("time[ms]")
plt.ylabel("y(t)")
plt.show()
def main():
i_gen = InputGenerator(0, T, dt)
data = i_gen.generate_lorenz()
num_train = int(len(data.T) * RATIO_TRAIN)
train_data = data.T[:num_train]
print(f"train_data_shape: {train_data.shape}")
model = ReservoirNetWork(inputs=train_data,
num_input_nodes=NUM_INPUT_NODES,
num_reservoir_nodes=NUM_RESERVOIR_NODES,
num_output_nodes=NUM_OUTPUT_NODES,
leak_rate=LEAK_RATE)
model.train()
train_result = model.get_train_result() # 訓練の結果を取得
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(data[0], data[1], data[2], marker='o')
ax.set_title("Echo State Network Sin Prediction")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
plt.show()
number_of_files = 10
base_var_num = 20
var_num = base_var_num
clause_num = base_var_num * 3
loop_exp_factor = 1.3
num_of_loops = 5
for i in range(1, num_of_loops + 1):
input_folder_name = 'inputs' + str(i)
output_folder_name = 'outputs' + str(i)
InputGenerator(num_of_var=var_num,
num_of_clause=clause_num).generate_and_write_inputs(
input_folder_name, number_of_files)
data_hash = {}
for file_num in range(number_of_files):
execute(input_folder_name, output_folder_name, file_num, data_hash)
lt = list()
lt.append(str(i) + ': ')
lt.append('var_num ' + str(var_num) + ' +- ' + str(var_num / 2))
lt.append('clause_num ' + str(clause_num) + ' +- ' +
str(clause_num / 2))
lt.append('for ' + str(number_of_files) + ' number_of_files\n')
for function_name in data_hash.keys():
def setInputGenerator(self, nb_timesteps, batch_size):
self.batch_size = batch_size
self.generator = InputGenerator(self.gt,
chunk_size=nb_timesteps,
batch_size=batch_size)
