def test_SimpleESN_initialization():
esn = SimpleESN(n_readout,
n_components=100,
damping=0.5,
weight_scaling=0.9,
discard_steps=0,
random_state=None)
echoes = esn.fit_transform(X)
assert_true(echoes.shape == (n_samples, n_readout))
X = loadtxt('MackeyGlass_t17.txt')
X = atleast_2d(X).T
train_length = 2000
test_length = 2000
X_train = X[:train_length]
y_train = X[1:train_length + 1]
X_test = X[train_length:train_length + test_length]
y_test = X[train_length + 1:train_length + test_length + 1]
# Simple training
my_esn = SimpleESN(n_readout=1000,
n_components=1000,
damping=0.3,
weight_scaling=1.25)
echo_train = my_esn.fit_transform(X_train)
regr = Ridge(alpha=0.01)
regr.fit(echo_train, y_train)
echo_test = my_esn.transform(X_test)
y_true, y_pred = y_test, regr.predict(echo_test)
err = mean_squared_error(y_true, y_pred)
fp = plt.figure(figsize=(12, 4))
trainplot = fp.add_subplot(1, 3, 1)
trainplot.plot(X_train[100:600], 'b')
trainplot.set_title('Some training signal')
echoplot = fp.add_subplot(1, 3, 2)
echoplot.plot(echo_train[100:600, :20])
echoplot.set_title('Some reservoir activation')
testplot = fp.add_subplot(1, 3, 3)
testplot.plot(X_test[-500:], 'b', label='test signal')
def test_SimpleESN_shape():
esn =SimpleESN(n_readout = n_readout)
echoes = esn.fit_transform(X)
assert_true(echoes.shape == (n_samples, n_readout))
def test_SimpleESN_initialization():
esn = SimpleESN(n_readout, n_components=100, damping=0.5,
weight_scaling=0.9, discard_steps=0, random_state=None)
echoes = esn.fit_transform(X)
assert_true(echoes.shape == (n_samples, n_readout))
def test_SimpleESN_incorrect_readout():
n_readout, n_components = 10, 5
esn =SimpleESN(n_readout = n_readout, n_components = n_components)
echoes = esn.fit_transform(X)
assert_true(echoes.shape == (n_samples, n_components))
def test_SimpleESN_discard():
discard_steps = 3
esn =SimpleESN(n_readout = n_readout, discard_steps = discard_steps)
echoes = esn.fit_transform(X)
assert_true(echoes.shape == (n_samples-discard_steps, n_readout))
# train_input = data[0, :-1]
# train_input=atleast_2d(train_input).T
# train_target = data[0, 1:]
# train_target=atleast_2d(train_target).T
# test_input=data[-1,:-1]
# test_input=atleast_2d(test_input).T
# test_target=data[-1,1:]
# test_target=atleast_2d(test_target).T
# Simple training
model_esn = SimpleESN(n_readout=1000,
n_components=1000,
damping=0.3,
weight_scaling=1.25)
echo_train_state = model_esn.fit_transform(train_input)
regr = Ridge(alpha=0.01)
regr.fit(echo_train_state, train_target)
# show the train result
echo_train_target, echo_train_pred = train_target, regr.predict(
echo_train_state)
err_train = mean_squared_error(echo_train_target, echo_train_pred)
data_figures = plt.figure(figsize=(12, 4))
trainplot = data_figures.add_subplot(1, 3, 1)
trainplot.plot(train_input[:], 'b')
trainplot.set_title('Training Signal')
echoplot = data_figures.add_subplot(1, 3, 2)
def test_SimpleESN_incorrect_readout():
n_readout, n_components = 10, 5
esn = SimpleESN(n_readout=n_readout, n_components=n_components)
echoes = esn.fit_transform(X)
assert_true(echoes.shape == (n_samples, n_components))
def test_SimpleESN_discard():
discard_steps = 3
esn = SimpleESN(n_readout=n_readout, discard_steps=discard_steps)
echoes = esn.fit_transform(X)
assert_true(echoes.shape == (n_samples - discard_steps, n_readout))
def test_SimpleESN_shape():
esn = SimpleESN(n_readout=n_readout)
echoes = esn.fit_transform(X)
assert_true(echoes.shape == (n_samples, n_readout))
from simple_esn import SimpleESN import numpy as np n_samples, n_features = 10, 5 np.random.seed(0) X = np.random.randn(n_samples, n_features) esn = SimpleESN(n_readout=2) echoes = esn.fit_transform(X)
if __name__ == '__main__':
X = loadtxt('MackeyGlass_t17.txt')
X = atleast_2d(X).T
train_length = 2000
test_length = 2000
X_train = X[:train_length]
y_train = X[1:train_length+1]
X_test = X[train_length:train_length+test_length]
y_test = X[train_length+1:train_length+test_length+1]
# Simple training
my_esn = SimpleESN(n_readout=1000, n_components=1000,
damping = 0.3, weight_scaling = 1.25)
echo_train = my_esn.fit_transform(X_train)
regr = Ridge(alpha = 0.01)
regr.fit(echo_train, y_train)
echo_test = my_esn.transform(X_test)
y_true, y_pred = y_test, regr.predict(echo_test)
err = mean_squared_error(y_true, y_pred)
fp = plt.figure(figsize=(12, 4))
trainplot = fp.add_subplot(1, 3, 1)
trainplot.plot(X_train[100:600], 'b')
trainplot.set_title('Some training signal')
echoplot = fp.add_subplot(1, 3, 2)
echoplot.plot(echo_train[100:600,:20])
echoplot.set_title('Some reservoir activation')
testplot = fp.add_subplot(1, 3, 3)
testplot.plot(X_test[-500:], 'b', label='test signal')