def test_SimpleESN_transform():
esn =SimpleESN(n_readout = n_readout)
echoes = esn.transform(X)
assert_true(esn.weights_ is not None)
assert_true(esn.input_weights_ is not None)
assert_true(esn.readout_idx_ is not None)
repeated_echoes = esn.transform(X)
assert_array_equal (echoes, repeated_echoes)
def test_SimpleESN_transform():
esn = SimpleESN(n_readout=n_readout)
echoes = esn.transform(X)
assert_true(esn.weights_ is not None)
assert_true(esn.input_weights_ is not None)
assert_true(esn.readout_idx_ is not None)
repeated_echoes = esn.transform(X)
assert_array_equal(echoes, repeated_echoes)
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')
testplot.plot(y_pred[-500:], 'g', label='prediction')
testplot.set_title('Prediction (MSE %0.3f)' % err)
testplot.legend(loc='lower right')
echoplot = data_figures.add_subplot(1, 3, 2)
# echoplot.plot(echo_train_state[0, :20])
echoplot.plot(echo_train_state[:, :8])
echoplot.set_title('Some Reservoir Activation')
testplot = data_figures.add_subplot(1, 3, 3)
testplot.plot(train_target[:], 'r', label='Target Signal')
testplot.plot(echo_train_pred[:], 'g', label='Prediction')
testplot.set_title('Prediction (MSE %0.8f)' % err_train)
testplot.legend(loc='upper right')
plt.tight_layout(0.5)
plt.savefig('MackeyGlass_ESN_Train_Prediction.png')
# show the test result
echo_test_state = model_esn.transform(test_input)
echo_test_target, echo_test_pred = test_target, regr.predict(
echo_test_state)
err_test = mean_squared_error(echo_test_target, echo_test_pred)
data_figures = plt.figure(figsize=(12, 4))
trainplot = data_figures.add_subplot(1, 3, 1)
trainplot.plot(test_input[:], 'b')
trainplot.set_title('Test Signal')
echoplot = data_figures.add_subplot(1, 3, 2)
echoplot.plot(echo_test_state[:, :8])
echoplot.set_title('Some reservoir activation')
testplot = data_figures.add_subplot(1, 3, 3)
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')
testplot.plot(y_pred[-500:], 'g', label='prediction')
testplot.set_title('Prediction (MSE %0.3f)' % err)
testplot.legend(loc='lower right')