class ESN_ATTN_simple:
def __init__(self, **kwargs):
self.conf = kwargs
self.sigmas = None
self.bias = True
self.W_i = None
self.N_i = None
self.N_o = None
self.N_f = 0
self.beta = 1e-6
self.method = 'ridge'
self.skip_con = kwargs.get('skip_con', 0)
self.activation = kwargs.get('activation', 'rbf')
self.N_h = kwargs.get('N_h')
self.sigma = kwargs.get('sigma', 1)
self.reservoirConf = kwargs.get('reservoirConf')
self.REncoder = ReservoirEncoder(self.reservoirConf)
self.encoder_func = self.REncoder.transform \
if kwargs.get('encoder', 'transform') == 'transform' \
else self.REncoder.echostate
@staticmethod
def pairwise_distances(X, Y):
D = -2 * X @ Y.T + np.sum(Y ** 2, axis=1) + np.sum(X ** 2, axis=1)[:, np.newaxis]
D[D < 0] = 0
return D
@staticmethod
def col_normalize(x, f=None):
if x.shape[0] == 0:
return x
fx = f(x) if f else x
fx_sum = fx.sum(axis=0).reshape([1, fx.shape[1]])
return fx / fx_sum
def _pre_output(self, X, Z=None):
"""
:param X: (N_i, N_samples)
:param Z: (nz, N_samples)
:param W_i: ((N_h, nz)
:return: H: (..., N_samples)
"""
assert(self.N_h > 0)
H = np.exp(
-self.pairwise_distances(self.W_i, Z.T) /
(2 * self.sigma ** 2 * self.sigmas.reshape((-1, 1)))
) # (N_h, N_samples)
H = np.vstack([H, Z])
self.N_f = self.N_h + Z.shape[0]
if self.skip_con:
H = np.vstack([H, X[-self.skip_con:]])
self.N_f += len(X[-self.skip_con:])
if self.bias:
ones = np.ones((1, H.shape[1]))
H = np.vstack([H, ones])
self.N_f += 1
return H
def train(self, X, Y, **kwargs):
num_prepare = kwargs.get('num_prepare', 0)
self.N_i = X.shape[0]
self.N_o = Y.shape[0]
Z = self.encoder_func(X) if self.REncoder else X
X, Z = X[:, num_prepare:], Z[:, num_prepare:]
if self.N_h <= 0:
self.W_i = np.empty((0, self.N_i))
else:
# self.W_i, _ = kmeans2(X.T, self.N_h, minit='points')
if self.activation == 'rbf':
if self.W_i is None:
self.W_i, self.sigmas = fcm(Z.T, self.N_h)
elif self.activation == 'rbf-random':
self.W_i = np.random.uniform(-1, 1, (self.N_h, self.N_i))
# self.W_i = X.T[np.random.choice(X.shape[1],self.N_h,replace=False)]
H = self._pre_output(X, Z)
self.W_o = ridge_regressor(H, Y, self.beta)
def predict(self, X, **kwargs):
num_prepare = kwargs.get('num_prepare', 0)
Z = self.encoder_func(X) if self.REncoder else X
X, Z = X[:, num_prepare:], Z[:, num_prepare:]
H = self._pre_output(X, Z)
return self.W_o.dot(H)
def predict_multistep(self, X, horizon):
Y = np.empty((self.N_o * horizon, X.shape[1]))
Z = np.vstack([X, Y])
start = X.shape[0]
Z[start:start + self.N_o, :] = self.predict(X)
for i in range(1, horizon):
Z[start + i * self.N_o: start + (i + 1) * self.N_o, :] = self.predict(Z[i * self.N_o: start + i * self.N_o, :])
return Z[start:, :]
def predict_multistep_esn(self, X, horizon):
Y = np.empty((self.N_o * horizon, 1))
Z = self.encoder_func(X)
z = Z[:, -1:]
x = X[:, -1:]
for i in range(horizon):
H = self._pre_output(x, z)
y = self.W_o.dot(H)
Y[i * self.N_o: (i + 1) * self.N_o] = y
x = np.vstack([x, y])[-self.N_i:]
z = self.REncoder.state_transition(z, y)
return Y
class ESN:
def __init__(self, **kwargs):
self.conf = kwargs
self.REncoder = None
self.bias = True
self.W_i = None
self.N_i = None
self.N_o = None
self.N_f = 0
self.beta = 1e-6
self.method = 'ridge'
self.skip_con = kwargs.get('skip_con', 0)
self.N_h = 0
self.reservoirConf = kwargs.get('reservoirConf')
self.REncoder = ReservoirEncoder(self.reservoirConf)
self.encoder_func = self.REncoder.echostate \
if kwargs.get('encoder', 'echostate') == 'echostate' \
else self.REncoder.transform
def _pre_output(self, X, Z=None):
"""
:param X: (N_i, N_samples)
:param Z: (nz, N_samples)
:param W_i: ((N_h, nz)
:return: H: (..., N_samples)
"""
H = Z
self.N_f = Z.shape[0]
if self.skip_con:
H = np.vstack([H, X[-self.skip_con:]])
self.N_f += len(X[-self.skip_con:])
if self.bias:
ones = np.ones((1, H.shape[1]))
H = np.vstack([H, ones])
self.N_f += 1
return H
def train(self, X, Y, **kwargs):
num_prepare = kwargs.get('num_prepare', 0)
self.N_i = X.shape[0]
self.N_o = Y.shape[0]
Z = self.encoder_func(X) if self.REncoder else X
X, Z = X[:, num_prepare:], Z[:, num_prepare:]
H = self._pre_output(X, Z)
self.W_o = ridge_regressor(H, Y, self.beta)
def predict(self, X, **kwargs):
num_prepare = kwargs.get('num_prepare', 0)
Z = self.encoder_func(X) if self.REncoder else X
X, Z = X[:, num_prepare:], Z[:, num_prepare:]
H = self._pre_output(X, Z)
return self.W_o.dot(H)
def predict_multistep(self, X, horizon):
Y = np.empty((self.N_o * horizon, X.shape[1]))
Z = np.vstack([X, Y])
start = X.shape[0]
Z[start:start + self.N_o, :] = self.predict(X)
for i in range(1, horizon):
# print(Z[i * self.N_o: start + i * self.N_o, :])
Z[start + i * self.N_o:start +
(i + 1) * self.N_o, :] = self.predict(Z[i * self.N_o:start +
i * self.N_o, :])
return Z[start:, :]
def predict_multistep_esn(self, X, horizon):
Y = np.empty((self.N_o * horizon, 1))
Z = self.encoder_func(X)
z = Z[:, -1:]
x = X[:, -1:]
for i in range(horizon):
H = self._pre_output(x, z)
y = self.W_o.dot(H)
Y[i * self.N_o:(i + 1) * self.N_o] = y
x = np.vstack([x, y])[-self.N_i:]
z = self.REncoder.state_transition(z, y)
# print(np.min(z), np.max(z))
return Y