def build_P_matrix(kohonen, features):
"""Build the projection matrix given a set of features from
the INPUT space of the Kohonen map. The projection is based
on the calculation of the mean of the selected features for
each node.
Parameters
---------
features: ndarray of integers
Numpy array of the features to be selected for the projection
If None all features are selected.
Return
------
P_matrix: ndarray
Numpy array of shape n_nodes, dtype= np.double giving the value
of the mean of the projected samples applied on the normalized
Kohonen matrix
"""
# Normalization of the Kohonen matrix is necessary for validity
debug("Build_P_matrix", "Starting the projection...")
KN = Normalizer().fit_transform(kohonen).tolil()
n_nodes, n_features = KN.shape
P_matrix = np.zeros(n_nodes)
# Slice over the rows of the matrix and build the projection
for i in np.arange(n_nodes):
selected_features = np.intersect1d(features,KN.rows[i])
data = np.asarray(KN.data[i], dtype=np.double)
P_matrix[i] = np.mean(data[selected_features])
return P_matrix
def build_P_matrix(kohonen, features):
"""Build the projection matrix given a set of features from
the INPUT space of the Kohonen map. The projection is based
on the calculation of the mean of the selected features for
each node.
Parameters
---------
features: ndarray of integers
Numpy array of the features to be selected for the projection
If None all features are selected.
Return
------
P_matrix: ndarray
Numpy array of shape n_nodes, dtype= np.double giving the value
of the mean of the projected samples applied on the normalized
Kohonen matrix
"""
# Normalization of the Kohonen matrix is necessary for validity
debug("Build_P_matrix", "Starting the projection...")
KN = Normalizer().fit_transform(kohonen).tolil()
n_nodes, n_features = KN.shape
P_matrix = np.zeros(n_nodes)
# Slice over the rows of the matrix and build the projection
for i in np.arange(n_nodes):
selected_features = np.intersect1d(features, KN.rows[i])
data = np.asarray(KN.data[i], dtype=np.double)
P_matrix[i] = np.mean(data[selected_features])
return P_matrix
def build_H_matrix(X, kohonen):
"""Build the reprentation of the hits matrix from the INPUT space into
the OUTPUT space, given the sample matrix X.
Required is to have the same sample set as used for training, that is
to say the same n_features dimensions.
Parameters
----------
X: array-like CSR matrix
Sparse representation of the samples in the INPUT space in a CSR
matrix of shape = [n_samples, n_features]. Must be the same as
that used for fitting the map.
Return
------
H_matrix : ndarray
Numpy array of shape = [n_nodes] of integers giving for each node
the number of best matching documents
"""
# Initialize the hits matrix as an ndarray of ints
debug("Build_H_matrix","Starting the counting of hits...")
debug("Build_H_matrix","Using %d documents with %d features" % X.shape)
n_nodes = kohonen.shape[0]
H_matrix = np.zeros(n_nodes, dtype = np.int)
KN = Normalizer().fit_transform(kohonen)
# Get the best matching units for all vectors
n_samples, n_features = X.shape
for i in xrange(n_samples):
bmu_idx = c_get_bmu(KN,X.getrow(i))[0]
H_matrix[bmu_idx]+=1
print bmu_idx
return H_matrix
def build_H_matrix(X, kohonen):
"""Build the reprentation of the hits matrix from the INPUT space into
the OUTPUT space, given the sample matrix X.
Required is to have the same sample set as used for training, that is
to say the same n_features dimensions.
Parameters
----------
X: array-like CSR matrix
Sparse representation of the samples in the INPUT space in a CSR
matrix of shape = [n_samples, n_features]. Must be the same as
that used for fitting the map.
Return
------
H_matrix : ndarray
Numpy array of shape = [n_nodes] of integers giving for each node
the number of best matching documents
"""
# Initialize the hits matrix as an ndarray of ints
debug("Build_H_matrix", "Starting the counting of hits...")
debug("Build_H_matrix", "Using %d documents with %d features" % X.shape)
n_nodes = kohonen.shape[0]
H_matrix = np.zeros(n_nodes, dtype=np.int)
KN = Normalizer().fit_transform(kohonen)
# Get the best matching units for all vectors
n_samples, n_features = X.shape
for i in xrange(n_samples):
bmu_idx = c_get_bmu(KN, X.getrow(i))[0]
H_matrix[bmu_idx] += 1
print bmu_idx
return H_matrix