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File Content: kernel_ridge.cpython-35.pyc

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�e�e���Z�d�S)zFModule :mod:`sklearn.kernel_ridge` implements kernel ridge regression.�����N����)�
BaseEstimator�RegressorMixin)�pairwise_kernels)�_solve_cholesky_kernel)�	check_X_y)�check_is_fittedc���������������@���ss���e��Z�d��Z�d�Z�d�d�d�d�d�d�d�d���Z�d�d�d	���Z�e�d
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��Kernel ridge regression.

Kernel ridge regression (KRR) combines ridge regression (linear least
    squares with l2-norm regularization) with the kernel trick. It thus
    learns a linear function in the space induced by the respective kernel and
    the data. For non-linear kernels, this corresponds to a non-linear
    function in the original space.

The form of the model learned by KRR is identical to support vector
    regression (SVR). However, different loss functions are used: KRR uses
    squared error loss while support vector regression uses epsilon-insensitive
    loss, both combined with l2 regularization. In contrast to SVR, fitting a
    KRR model can be done in closed-form and is typically faster for
    medium-sized datasets. On the other  hand, the learned model is non-sparse
    and thus slower than SVR, which learns a sparse model for epsilon > 0, at
    prediction-time.

This estimator has built-in support for multi-variate regression
    (i.e., when y is a 2d-array of shape [n_samples, n_targets]).

Read more in the :ref:`User Guide <kernel_ridge>`.

Parameters
    ----------
    alpha : {float, array-like}, shape = [n_targets]
        Small positive values of alpha improve the conditioning of the problem
        and reduce the variance of the estimates.  Alpha corresponds to
        ``(2*C)^-1`` in other linear models such as LogisticRegression or
        LinearSVC. If an array is passed, penalties are assumed to be specific
        to the targets. Hence they must correspond in number.

kernel : string or callable, default="linear"
        Kernel mapping used internally. A callable should accept two arguments
        and the keyword arguments passed to this object as kernel_params, and
        should return a floating point number.

gamma : float, default=None
        Gamma parameter for the RBF, laplacian, polynomial, exponential chi2
        and sigmoid kernels. Interpretation of the default value is left to
        the kernel; see the documentation for sklearn.metrics.pairwise.
        Ignored by other kernels.

degree : float, default=3
        Degree of the polynomial kernel. Ignored by other kernels.

coef0 : float, default=1
        Zero coefficient for polynomial and sigmoid kernels.
        Ignored by other kernels.

kernel_params : mapping of string to any, optional
        Additional parameters (keyword arguments) for kernel function passed
        as callable object.

Attributes
    ----------
    dual_coef_ : array, shape = [n_samples] or [n_samples, n_targets]
        Representation of weight vector(s) in kernel space

X_fit_ : {array-like, sparse matrix}, shape = [n_samples, n_features]
        Training data, which is also required for prediction

References
    ----------
    * Kevin P. Murphy
      "Machine Learning: A Probabilistic Perspective", The MIT Press
      chapter 14.4.3, pp. 492-493

See also
    --------
    Ridge
        Linear ridge regression.
    SVR
        Support Vector Regression implemented using libsvm.

Examples
    --------
    >>> from sklearn.kernel_ridge import KernelRidge
    >>> import numpy as np
    >>> n_samples, n_features = 10, 5
    >>> rng = np.random.RandomState(0)
    >>> y = rng.randn(n_samples)
    >>> X = rng.randn(n_samples, n_features)
    >>> clf = KernelRidge(alpha=1.0)
    >>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
    KernelRidge(alpha=1.0, coef0=1, degree=3, gamma=None, kernel='linear',
                kernel_params=None)
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���r���r���r���)r����X�YZparamsr���r���r����_get_kernelq���s����		zKernelRidge._get_kernelc�������������C���s
���|��j��d�k�S)N�precomputed)r���)r���r���r���r����	_pairwise{���s����zKernelRidge._pairwisec����������	���C���s����t��|�|�d�d
�d�d�d�d��\�}�}�|��j�|���}�t�j�|��j���}�d�}�t�|�j���d�k�r{�|�j�d�d���}�d�}�|��j�d	�k�}�t	�|�|�|�|�|���|��_
�|�r��|��j
�j����|��_
�|�|��_�|��S)a���Fit Kernel Ridge regression model

Parameters
        ----------
        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
            Training data

y : array-like, shape = [n_samples] or [n_samples, n_targets]
            Target values

sample_weight : float or numpy array of shape [n_samples]
            Individual weights for each sample, ignored if None is passed.

Returns
        -------
        self : returns an instance of self.
        Z
accept_sparse�csr�cscZmulti_outputTZ	y_numericFr���r���)r���r������)
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atleast_1dr����len�shapeZreshaper���r����
dual_coef_�ravel�X_fit_)r���r����yZ
sample_weight�Kr���r"����copyr���r���r����fit���s ����	zKernelRidge.fitc�������������C���s;���t��|��d�d�g���|��j�|�|��j���}�t�j�|�|��j���S)a:��Predict using the kernel ridge model

Parameters
        ----------
        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
            Samples.

Returns
        -------
        C : array, shape = [n_samples] or [n_samples, n_targets]
            Returns predicted values.
        r#���r!���)r���r���r#���r����dotr!���)r���r���r%���r���r���r����predict����s����
zKernelRidge.predict)
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