sklearn.svm¶
sklearn.svm.LinearSVC¶
This class provides Linear Support Vector Classification.
Methods¶
sklearn.svm.LinearSVC.fit¶
sklearn.svm.LinearSVC. fit (X, y, sample_weight=None)
Supported Arguments
X: NumPy Array or Pandas Dataframes.y: NumPy Array.sample_weight: Numeric NumPy Array (only if data is not distributed)
sklearn.svm.LinearSVC.predict¶
sklearn.svm.LinearSVC. predict (X)
Supported Arguments
X: NumPy Array or Pandas Dataframes.
sklearn.svm.LinearSVC.score¶
sklearn.svm.LinearSVC. score (X, y, sample_weight=None)
Supported Arguments
X: NumPy Array or Pandas Dataframes.y: NumPy Array or Pandas Dataframes.sample_weight: Numeric NumPy Array or Pandas Dataframes.
Example Usage:¶
>>> import bodo
>>> import numpy as np
>>> from sklearn.svm import LinearSVC
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_features=4, random_state=0)
>>> @bodo.jit
... def test_linearsvc(X, y):
... scaler = StandardScaler()
... scaler.fit(X)
... X = scaler.transform(X)
... clf = LinearSVC()
... clf.fit(X, y)
... ans = clf.predict(np.array([[0, 0, 0, 0]]))
... print(ans)
...
>>> test_linearsvc(X, y)
[1]