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Fix docs
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@janoPig
janoPig committed Jan 28, 2024
1 parent 84f6796 commit 046a451
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Showing 4 changed files with 45 additions and 45 deletions.
26 changes: 13 additions & 13 deletions HROCH/classifier.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -190,14 +190,14 @@ def fit(self, X, y, sample_weight=None, check_input=True):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : numpy.ndarray of shape (n_samples,)
y : array-like of shape (n_samples,)
Target vector relative to X. Needs samples of 2 classes.
sample_weight : numpy.ndarray of shape (n_samples,) default=None
sample_weight : array-like of shape (n_samples,) default=None
Array of weights that are assigned to individual samples.
If not provided, then each sample is given unit weight.
Expand Down Expand Up @@ -237,7 +237,7 @@ def predict(self, X, id=None, check_input=True, use_parsed_model=True):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
The input samples.
id : int
Expand All @@ -249,7 +249,7 @@ def predict(self, X, id=None, check_input=True, use_parsed_model=True):
Returns
-------
y : numpy.ndarray of shape (n_samples,)
y : ndarray of shape (n_samples,)
The predicted classes.
"""
preds = super(NonlinearLogisticRegressor, self).predict(X, id, check_input=check_input, use_parsed_model=use_parsed_model)
Expand All @@ -261,15 +261,15 @@ def predict_proba(self, X, id=None, check_input=True):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you're doing.
Returns
-------
p : ndarray of shape (n_samples, n_classes)
T : ndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
Expand Down Expand Up @@ -298,11 +298,11 @@ def fit(self, X, y):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features. Should be in the range [0, 1].
y : numpy.ndarray of shape (n_samples,)
y : array-like of shape (n_samples,)
Target vector relative to X.
Returns
Expand All @@ -320,12 +320,12 @@ def predict(self, X):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
The input samples.
Returns
-------
y : numpy.ndarray of shape (n_samples,)
y : ndarray of shape (n_samples,)
The predicted classes.
"""
return super().predict(X)
Expand All @@ -336,11 +336,11 @@ def predict_proba(self, X):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : narray-like of shape (n_samples, n_features)
Returns
-------
p : ndarray of shape (n_samples, n_classes)
T : ndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
Expand Down
26 changes: 13 additions & 13 deletions HROCH/fuzzy.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -179,14 +179,14 @@ def fit(self, X, y, sample_weight=None, check_input=True):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features. Should be in the range [0, 1].
y : numpy.ndarray of shape (n_samples,)
y : array-like of shape (n_samples,)
Target vector relative to X. Needs samples of 2 classes.
sample_weight : numpy.ndarray of shape (n_samples,) default=None
sample_weight : array-like of shape (n_samples,) default=None
Array of weights that are assigned to individual samples.
If not provided, then each sample is given unit weight.
Expand Down Expand Up @@ -225,7 +225,7 @@ def predict(self, X, id=None, check_input=True, use_parsed_model=True):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
The input samples.
id : int
Expand All @@ -237,7 +237,7 @@ def predict(self, X, id=None, check_input=True, use_parsed_model=True):
Returns
-------
y : numpy.ndarray of shape (n_samples,)
y : ndarray of shape (n_samples,)
The predicted classes.
"""
preds = super(FuzzyRegressor, self).predict(X, id, check_input=check_input, use_parsed_model=use_parsed_model)
Expand All @@ -249,15 +249,15 @@ def predict_proba(self, X, id=None, check_input=True):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you're doing.
Returns
-------
p : ndarray of shape (n_samples, n_classes)
T : ndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
Expand Down Expand Up @@ -290,11 +290,11 @@ def fit(self, X, y):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features. Should be in the range [0, 1].
y : numpy.ndarray of shape (n_samples,)
y : array-like of shape (n_samples,)
Target vector relative to X.
Returns
Expand All @@ -312,12 +312,12 @@ def predict(self, X):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
The input samples.
Returns
-------
y : numpy.ndarray of shape (n_samples,)
y : ndarray of shape (n_samples,)
The predicted classes.
"""
return super().predict(X)
Expand All @@ -328,11 +328,11 @@ def predict_proba(self, X):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Returns
-------
p : ndarray of shape (n_samples, n_classes)
T : ndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
Expand Down
30 changes: 15 additions & 15 deletions HROCH/hroch.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -193,14 +193,14 @@ def fit(self, X, y, sample_weight=None, check_input=True):
Parameters
----------
X : ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : ndarray of shape (n_samples,)
y : array-like of shape (n_samples,)
Target vector relative to X.
sample_weight : ndarray of shape (n_samples,) default=None
sample_weight : array-like of shape (n_samples,) default=None
Array of weights that are assigned to individual samples.
If not provided, then each sample is given unit weight.
Expand Down Expand Up @@ -232,7 +232,7 @@ def predict(self, X, check_input=True):
Parameters
----------
X : ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
The input samples.
check_input : bool, default=True
Expand Down Expand Up @@ -278,14 +278,14 @@ def fit(self, X, y, sample_weight=None, check_input=True):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : numpy.ndarray of shape (n_samples,)
y : array-like of shape (n_samples,)
Target vector relative to X. Needs samples of 2 classes.
sample_weight : numpy.ndarray of shape (n_samples,) default=None
sample_weight : array-like of shape (n_samples,) default=None
Array of weights that are assigned to individual samples.
If not provided, then each sample is given unit weight.
Expand Down Expand Up @@ -338,7 +338,7 @@ def predict(self, X, check_input=True):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
The input samples.
check_input : bool, default=True
Expand All @@ -347,7 +347,7 @@ def predict(self, X, check_input=True):
Returns
-------
y : numpy.ndarray of shape (n_samples,)
y : ndarray of shape (n_samples,)
The predicted classes.
"""
preds = self._predict(X, check_input=check_input)
Expand All @@ -359,15 +359,15 @@ def predict_proba(self, X, check_input=True):
Parameters
----------
X : numpy.ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you're doing.
Returns
-------
p : ndarray of shape (n_samples, n_classes)
T : ndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
Expand Down Expand Up @@ -647,14 +647,14 @@ def fit(self, X, y, sample_weight = None, check_input=True):
Parameters
----------
X : ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : ndarray of shape (n_samples,)
y : array-like of shape (n_samples,)
Target vector relative to X.
sample_weight : ndarray of shape (n_samples,) default=None
sample_weight : array-like of shape (n_samples,) default=None
Array of weights that are assigned to individual samples.
If not provided, then each sample is given unit weight.
Expand Down Expand Up @@ -812,7 +812,7 @@ def predict(self, X, id=None, check_input=True, use_parsed_model=True):
Parameters
----------
X : ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
The input samples.
id : int
Expand Down
8 changes: 4 additions & 4 deletions HROCH/regressor.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -173,14 +173,14 @@ def fit(self, X, y, sample_weight=None, check_input=True):
Parameters
----------
X : ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : ndarray of shape (n_samples,)
y : array-like of shape (n_samples,)
Target vector relative to X.
sample_weight : ndarray of shape (n_samples,) default=None
sample_weight : array-like of shape (n_samples,) default=None
Array of weights that are assigned to individual samples.
If not provided, then each sample is given unit weight.
Expand All @@ -203,7 +203,7 @@ def predict(self, X, id=None, check_input=True, use_parsed_model=True):
Parameters
----------
X : ndarray of shape (n_samples, n_features)
X : array-like of shape (n_samples, n_features)
The input samples.
id : int
Expand Down

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