Python源码示例：sklearn.utils.validation.NotFittedError()
示例1
def predict(self, X):
"""Perform classification on samples in X.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Matrix containing new samples
Returns
-------
y_pred : array, shape = [n_samples]
The value of prediction for each sample
"""
if self.is_fitted == False:
raise NotFittedError("This KOMD instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.")
X = check_array(X, accept_sparse='csr', dtype=np.float64, order="C")
if self.multiclass_ == True:
return self.cls.predict(X)
return np.array([self.classes_[1] if p >=0 else self.classes_[0] for p in self.decision_function(X)])
示例2
def decision_function(self, X):
"""Distance of the samples in X to the separating hyperplane.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
Z : array-like, shape = [n_samples, 1]
Returns the decision function of the samples.
"""
if self.is_fitted == False:
raise NotFittedError("This KOMD instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.")
X = check_array(X, accept_sparse='csr', dtype=np.float64, order="C")
if self.multiclass_ == True:
return self.cls.decision_function(X)
Kf = self.__kernel_definition__()
YY = matrix(np.diag(list(matrix(self.Y))))
ker_matrix = matrix(Kf(X,self.X).astype(np.double))
z = ker_matrix*YY*self.gamma
z = z-self.bias
return np.array(list(z))
示例3
def transform(self, X, y=None):
"""Use the model to transform matrix to Shared Response space
Parameters
----------
X : list of 2D arrays, element i has shape=[voxels_i, samples_i]
Each element in the list contains the fMRI data of one subject
note that number of voxels and samples can vary across subjects
y : not used (as it is unsupervised learning)
Returns
-------
s : list of 2D arrays, element i has shape=[features_i, samples_i]
Shared responses from input data (X)
"""
# Check if the model exist
if hasattr(self, 'w_') is False:
raise NotFittedError("The model fit has not been run yet.")
# Check the number of subjects
if len(X) != len(self.w_):
raise ValueError("The number of subjects does not match the one"
" in the model.")
s = [None] * len(X)
for subject in range(len(X)):
s[subject] = self.w_[subject].T.dot(X[subject])
return s
示例4
def transform(self, X, y=None):
"""Use the model to transform data to the Shared Response subspace
Parameters
----------
X : list of 2D arrays, element i has shape=[voxels_i, samples_i]
Each element in the list contains the fMRI data of one subject.
y : not used
Returns
-------
s : list of 2D arrays, element i has shape=[features_i, samples_i]
Shared responses from input data (X)
"""
# Check if the model exist
if hasattr(self, 'w_') is False:
raise NotFittedError("The model fit has not been run yet.")
# Check the number of subjects
if len(X) != len(self.w_):
raise ValueError("The number of subjects does not match the one"
" in the model.")
s = [None] * len(X)
for subject in range(len(X)):
s[subject] = self.w_[subject].T.dot(X[subject])
return s
示例5
def transform(self, X):
"""Use the model to transform new data to Shared Response space
Parameters
----------
X : list of 2D arrays, element i has shape=[voxels_i, timepoints_i]
Each element in the list contains the fMRI data of one subject.
Returns
-------
r : list of 2D arrays, element i has shape=[features_i, timepoints_i]
Shared responses from input data (X)
s : list of 2D arrays, element i has shape=[voxels_i, timepoints_i]
Individual data obtained from fitting model to input data (X)
"""
# Check if the model exist
if hasattr(self, 'w_') is False:
raise NotFittedError("The model fit has not been run yet.")
# Check the number of subjects
if len(X) != len(self.w_):
raise ValueError("The number of subjects does not match the one"
" in the model.")
r = [None] * len(X)
s = [None] * len(X)
for subject in range(len(X)):
if X[subject] is not None:
r[subject], s[subject] = self._transform_new_data(X[subject],
subject)
return r, s
示例6
def transform_subject(self, X):
"""Transform a new subject using the existing model
Parameters
----------
X : 2D array, shape=[voxels, timepoints]
The fMRI data of the new subject.
Returns
-------
w : 2D array, shape=[voxels, features]
Orthogonal mapping `W_{new}` for new subject
s : 2D array, shape=[voxels, timepoints]
Individual term `S_{new}` for new subject
"""
# Check if the model exist
if hasattr(self, 'w_') is False:
raise NotFittedError("The model fit has not been run yet.")
# Check the number of TRs in the subject
if X.shape[1] != self.r_.shape[1]:
raise ValueError("The number of timepoints(TRs) does not match the"
"one in the model.")
s = np.zeros_like(X)
for i in range(self.n_iter):
w = self._update_transform_subject(X, s, self.r_)
s = self._shrink(X - w.dot(self.r_), self.lam)
return w, s
示例7
def transform(self, X, y=None):
"""Use the model to transform matrix to Shared Response space
Parameters
----------
X : list of 2D arrays, element i has shape=[voxels_i, samples_i]
Each element in the list contains the fMRI data of one subject
note that number of voxels and samples can vary across subjects.
y : not used as it only applies the mappings
Returns
-------
s : list of 2D arrays, element i has shape=[features_i, samples_i]
Shared responses from input data (X)
"""
# Check if the model exist
if hasattr(self, 'w_') is False:
raise NotFittedError("The model fit has not been run yet.")
# Check the number of subjects
if len(X) != len(self.w_):
raise ValueError("The number of subjects does not match the one"
" in the model.")
s = [None] * len(X)
for subject in range(len(X)):
s[subject] = self.w_[subject].T.dot(X[subject])
return s
示例8
def predict(self, X):
"""Classify the output for given data
Parameters
----------
X : list of 2D arrays, element i has shape=[voxels_i, samples_i]
Each element in the list contains the fMRI data of one subject
The number of voxels should be according to each subject at
the moment of training the model.
Returns
-------
p: list of arrays, element i has shape=[samples_i]
Predictions for each data sample.
"""
# Check if the model exist
if hasattr(self, 'w_') is False:
raise NotFittedError("The model fit has not been run yet.")
# Check the number of subjects
if len(X) != len(self.w_):
raise ValueError("The number of subjects does not match the one"
" in the model.")
X_shared = self.transform(X)
p = [None] * len(X_shared)
for subject in range(len(X_shared)):
sumexp, _, exponents = utils.sumexp_stable(
self.theta_.T.dot(X_shared[subject]) + self.bias_)
p[subject] = self.classes_[
(exponents / sumexp[np.newaxis, :]).argmax(axis=0)]
return p
示例9
def check_is_fitted(estimator, attribute):
if not hasattr(estimator, attribute):
raise NotFittedError(
"This %s instance is not fitted yet. Call 'fit' with "
"appropriate arguments before using this method."
% type(estimator).__name__)
