def _compute_A(X, pi, classes):
""" Compute the A matrix in the variance estimation technique.
Parameters
----------
X : array
The feature matrix.
pi : array
The probability matrix predicted by the classifier.
classes : array
The list of class names ordered lexicographically.
Returns
-------
A : array
The A matrix as part of the variance calcucation.
"""
n_classes = len(classes)
n_features = X.shape[1]
n_samples = X.shape[0]
width = n_classes * n_features
one_in_k = LabelBinarizer(pos_label=1, neg_label=0).fit_transform(classes)
I_same = one_in_k.repeat(n_features, axis=0)
I_same = np.tile(I_same, n_samples)
I_diff = 1 - I_same
A = np.tile(pi.flatten(), (width, 1))
B = 1 - A
C = -A
D = pi.transpose().repeat(n_features, axis=0).repeat(n_classes, axis=1)
E = X.transpose().repeat(n_classes, axis=1)
E = np.tile(E, (n_classes, 1))
G = A * B * I_same + C * D * I_diff
G = E * G
outer = np.dot(G, G.transpose())
return outer
def _compute_A(X, pi, classes):
""" Compute the A matrix in the variance estimation technique.
Parameters
----------
X : array
The feature matrix.
pi : array
The probability matrix predicted by the classifier.
classes : array
The list of class names ordered lexicographically.
Returns
-------
A : array
The A matrix as part of the variance calcucation.
"""
n_classes = len(classes)
n_features = X.shape[1]
n_samples = X.shape[0]
width = n_classes * n_features
one_in_k = LabelBinarizer(pos_label=1, neg_label=0).fit_transform(classes)
I_same = one_in_k.repeat(n_features, axis=0)
I_same = np.tile(I_same, n_samples)
I_diff = 1 - I_same
A = np.tile(pi.flatten(), (width, 1))
B = 1 - A
C = -A
D = pi.transpose().repeat(n_features, axis=0).repeat(n_classes, axis=1)
E = X.transpose().repeat(n_classes, axis=1)
E = np.tile(E, (n_classes, 1))
G = A * B * I_same + C * D * I_diff
G = E * G
outer = np.dot(G, G.transpose())
return outer
