def partial_fit(self, X, y=None):
"""Extend the model by X as additional training data.
Parameters
----------
X : {array-like, sparse matrix}
Training data. Shape = [n_samples, n_features]
y : list, optional (default = None)
List of classes for the given input of X. Size have to be n_samples."""
if y is not None:
if self._y_is_csr:
self._y = vstack([self._y, y])
else:
self._y = np.concatenate((self._y, y), axis=0)
X_csr = csr_matrix(X)
instances, features = X_csr.nonzero()
data = X_csr.data
for i in xrange(len(instances)):
instances[i] += self._index_elements_count
self._index_elements_count += X.shape[0]
self._pointer_address_of_nearestNeighbors_object = _nearestNeighbors.fit(instances.tolist(), features.tolist(), data.tolist(),
self._pointer_address_of_nearestNeighbors_object)
def partial_fit(self, X, y=None):
"""Extend the model by X as additional training data.
Parameters
----------
X : {array-like, sparse matrix}
Training data. Shape = [n_samples, n_features]
y : list, optional (default = None)
List of classes for the given input of X. Size have to be n_samples."""
if y is not None:
if self._y_is_csr:
self._y = vstack([self._y, y])
else:
self._y = np.concatenate((self._y, y), axis=0)
X_csr = csr_matrix(X)
instances, features = X_csr.nonzero()
data = X_csr.data
for i in xrange(len(instances)):
instances[i] += self._index_elements_count
self._index_elements_count += X.shape[0]
self._pointer_address_of_nearestNeighbors_object = _nearestNeighbors.fit(
instances.tolist(), features.tolist(), data.tolist(),
self._pointer_address_of_nearestNeighbors_object)
def fit(self, X, y=None):
"""Fit the model using X as training data.
Parameters
----------
X : {array-like, sparse matrix}, optional
Training data. If array or matrix, shape = [n_samples, n_features]
If X is None, a "lazy fitting" is performed. If kneighbors is called, the fitting
with with the data there is done. Also the caching of computed hash values is deactivated in
this case.
y : list, optional (default = None)
List of classes for the given input of X. Size have to be n_samples."""
if y is not None:
self._y_is_csr = True
_, self._y = check_X_y(X, y, "csr", multi_output=True)
if self._y.ndim == 1 or self._y.shape[1] == 1:
self._y_is_csr = False
else:
self._y_is_csr = False
X_csr = csr_matrix(X)
self._index_elements_count = X_csr.shape[0]
instances, features = X_csr.nonzero()
maxFeatures = int(max(X_csr.getnnz(1)))
data = X_csr.data
# returns a pointer to the inverse index stored in c++
self._pointer_address_of_nearestNeighbors_object = _nearestNeighbors.fit(instances.tolist(), features.tolist(), data.tolist(),
X_csr.shape[0], maxFeatures,
self._pointer_address_of_nearestNeighbors_object)
def fit(self, X, y=None):
"""Fit the model using X as training data.
Parameters
----------
X : {array-like, sparse matrix}, optional
Training data. If array or matrix, shape = [n_samples, n_features]
If X is None, a "lazy fitting" is performed. If kneighbors is called, the fitting
with with the data there is done. Also the caching of computed hash values is deactivated in
this case.
y : list, optional (default = None)
List of classes for the given input of X. Size have to be n_samples."""
if y is not None:
self._y_is_csr = True
_, self._y = check_X_y(X, y, "csr", multi_output=True)
if self._y.ndim == 1 or self._y.shape[1] == 1:
self._y_is_csr = False
else:
self._y_is_csr = False
X_csr = csr_matrix(X)
self._index_elements_count = X_csr.shape[0]
instances, features = X_csr.nonzero()
maxFeatures = int(max(X_csr.getnnz(1)))
data = X_csr.data
# returns a pointer to the inverse index stored in c++
self._pointer_address_of_nearestNeighbors_object = _nearestNeighbors.fit(
instances.tolist(), features.tolist(), data.tolist(),
X_csr.shape[0], maxFeatures,
self._pointer_address_of_nearestNeighbors_object)
