Login Register

sklearn.metrics.pairwise.sigmoid_kernel()

sklearn.metrics.pairwise.sigmoid_kernel(X, Y=None, gamma=None, coef0=1) [source]

Compute the sigmoid kernel between X and Y:

K(X, Y) = tanh(gamma <X, Y> + coef0)

Read more in the User Guide.

Parameters:

X : ndarray of shape (n_samples_1, n_features)

Y : ndarray of shape (n_samples_2, n_features)

gamma : float, default None

If None, defaults to 1.0 / n_samples_1

coef0 : int, default 1

Returns:

Gram matrix : array of shape (n_samples_1, n_samples_2)

Links:

http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.sigmoid_kernel.html

doc_scikit_learn

2017-01-15 04:26:37

Comments

Leave a Comment

Please login to continue.

Popular Articles

sklearn.metrics.pairwise.pairwise_kernels() sklearn.metrics.pairwise.pairwise_kernels(X, Y=None, metric='linear', filter_params=False, n_jobs=1, **kwds) [source] Compute the kernel between

sklearn.metrics.pairwise.kernel_metrics() sklearn.metrics.pairwise.kernel_metrics() [source] Valid metrics for pairwise_kernels This function simply returns the valid pairwise distance me

sklearn.metrics.pairwise.laplacian_kernel() sklearn.metrics.pairwise.laplacian_kernel(X, Y=None, gamma=None) [source] Compute the laplacian kernel between X and Y. The laplacian kernel is d

sklearn.metrics.pairwise.chi2_kernel() sklearn.metrics.pairwise.chi2_kernel(X, Y=None, gamma=1.0) [source] Computes the exponential chi-squared kernel X and Y. The chi-squared kernel i

sklearn.metrics.pairwise.polynomial_kernel() sklearn.metrics.pairwise.polynomial_kernel(X, Y=None, degree=3, gamma=None, coef0=1) [source] Compute the polynomial kernel between X and Y: K(X,