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import numpy as np
from scipy import special
def normalize(a, axis=None):
"""
Normalize the input array so that it sums to 1.
Parameters
----------
a : array
Non-normalized input data.
axis : int
Dimension along which normalization is performed.
Notes
-----
Modifies the input **inplace**.
"""
a_sum = a.sum(axis)
if axis and a.ndim > 1:
# Make sure we don't divide by zero.
a_sum[a_sum == 0] = 1
shape = list(a.shape)
shape[axis] = 1
a_sum.shape = shape
a /= a_sum
def log_normalize(a, axis=None):
"""
Normalize the input array so that ``sum(exp(a)) == 1``.
Parameters
----------
a : array
Non-normalized input data.
axis : int
Dimension along which normalization is performed.
Notes
-----
Modifies the input **inplace**.
"""
if axis is not None and a.shape[axis] == 1:
# Handle single-state GMMHMM in the degenerate case normalizing a
# single -inf to zero.
a[:] = 0
else:
with np.errstate(under="ignore"):
a_lse = special.logsumexp(a, axis, keepdims=True)
a -= a_lse
def fill_covars(covars, covariance_type='full', n_components=1, n_features=1):
if covariance_type == 'full':
return covars
elif covariance_type == 'diag':
return np.array(list(map(np.diag, covars)))
elif covariance_type == 'tied':
return np.tile(covars, (n_components, 1, 1))
elif covariance_type == 'spherical':
# Regardless of what is passed in, we flatten in
# and then expand it to the correct shape
covars = np.ravel(covars)
eye = np.eye(n_features)[np.newaxis, :, :]
covars = covars[:, np.newaxis, np.newaxis]
return eye * covars
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