sigmoid_kernel#

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

计算 X 和 Y 之间的 sigmoid 核。

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

Read more in the User Guide.

参数:

X{array-like, sparse matrix} of shape (n_samples_X, n_features): 特征数组。
Y{array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None: 可选的第二个特征数组。如果为 None，则使用 Y=X。
gammafloat, default=None: Coefficient of the vector inner product. If None, defaults to 1.0 / n_features.
coef0float, default=1: Constant offset added to scaled inner product.

返回:

kernel形状为 (n_samples_X, n_samples_Y) 的 ndarray: Sigmoid kernel between two arrays.

示例

>>> from sklearn.metrics.pairwise import sigmoid_kernel
>>> X = [[0, 0, 0], [1, 1, 1]]
>>> Y = [[1, 0, 0], [1, 1, 0]]
>>> sigmoid_kernel(X, Y)
array([[0.76, 0.76],
       [0.87, 0.93]])

sigmoid_kernel#

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