import matplotlib
matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
n_h = 50
time_steps = 100
random_state = np.random.RandomState(1999)
sin = np.sin(np.linspace(-3 * np.pi, 3 * np.pi, time_steps))[:, None]
cos = np.cos(np.linspace(-3 * np.pi, 3 * np.pi, time_steps))[:, None]
seq = np.concatenate((sin, cos), axis=-1)[None]
clf = GMMRNN(learning_alg="rmsprop", n_mixture_components=20,
hidden_layer_sizes=[n_h],
max_iter=1000, learning_rate=.0001,
bidirectional=False, momentum=0.99,
recurrent_activation="lstm", minibatch_size=10,
save_frequency=1000, random_seed=1999)
shp = seq.shape
seq_r = seq.reshape(shp[0] * shp[1], shp[-1])
mean = seq_r.mean(axis=0, keepdims=True)
std = seq_r.std(axis=0, keepdims=True)
seq = seq - mean
seq = seq / std
clf.fit(seq)
t1 = clf.sample(n_steps=time_steps)
t2 = clf.force_sample(seq[0])
t3 = clf.sample(bias=0., n_steps=time_steps)
import matplotlib
matplotlib.use('Agg')
from minet.datasets import plot_scatter_iamondb_example
from minet.datasets import plot_lines_iamondb_example
from minet.datasets import fetch_iamondb
import matplotlib.pyplot as plt
from minet import GMMRNN
import numpy as np
X, y = fetch_iamondb()
clf = GMMRNN(learning_alg="rmsprop", n_mixture_components=20,
hidden_layer_sizes=[1000],
max_iter=20000, learning_rate=.00001,
bidirectional=False, momentum=0.99,
recurrent_activation="lstm", minibatch_size=1000,
save_frequency=100, random_seed=1999)
seq = X[0][:, 1:]
#seq = seq[1:] - seq[:-1]
seq = seq[:100]
mi0 = seq.min(axis=0)
ma0 = seq.max(axis=0)
seq = (seq - mi0) / (ma0 - mi0)
clf.fit(seq)
import matplotlib.pyplot as plt
n_h = 50
time_steps = 100
random_state = np.random.RandomState(1999)
sin = np.sin(np.linspace(-3 * np.pi, 3 * np.pi, time_steps))[:, None]
cos = np.cos(np.linspace(-3 * np.pi, 3 * np.pi, time_steps))[:, None]
seq = np.concatenate((sin, cos), axis=-1)[None]
clf = GMMRNN(learning_alg="rmsprop",
n_mixture_components=20,
hidden_layer_sizes=[n_h],
max_iter=1000,
learning_rate=.0001,
bidirectional=False,
momentum=0.99,
recurrent_activation="lstm",
minibatch_size=10,
save_frequency=1000,
random_seed=1999)
shp = seq.shape
seq_r = seq.reshape(shp[0] * shp[1], shp[-1])
mean = seq_r.mean(axis=0, keepdims=True)
std = seq_r.std(axis=0, keepdims=True)
seq = seq - mean
seq = seq / std
clf.fit(seq)
