sklearn.svm¶

sklearn.svm.LinearSVC¶

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]