ML之LiR：使用线性回归LiR回归模型在披萨数据集上拟合(train)、价格回归预测(test)

ML之LiR：使用线性回归LiR回归模型在披萨数据集上拟合(train)、价格回归预测(test)

目录

输出结果

设计思路

核心代码

输出结果

设计思路

核心代码

r= LinearRegression()
r.fit(X_train, y_train)
 
x = np.linspace(0, 26, 100)
x = x.reshape(xx.shape[0], 1)
y = r.predict(x)

class LinearRegression(LinearModel, RegressorMixin):
    """
    Ordinary least squares Linear Regression.
    
    Parameters
    ----------
    fit_intercept : boolean, optional, default True
    whether to calculate the intercept for this model. If set
    to False, no intercept will be used in calculations
    (e.g. data is expected to be already centered).
    
    normalize : boolean, optional, default False
    This parameter is ignored when ``fit_intercept`` is set to 
     False.
    If True, the regressors X will be normalized before 
     regression by
    subtracting the mean and dividing by the l2-norm.
    If you wish to standardize, please use
    :class:`sklearn.preprocessing.StandardScaler` before 
     calling ``fit`` on
    an estimator with ``normalize=False``.
    
    copy_X : boolean, optional, default True
    If True, X will be copied; else, it may be overwritten.
    
    n_jobs : int, optional, default 1
    The number of jobs to use for the computation.
    If -1 all CPUs are used. This will only provide speedup for
    n_targets > 1 and sufficient large problems.
    
    Attributes
    ----------
    coef_ : array, shape (n_features, ) or (n_targets, n_features)
    Estimated coefficients for the linear regression problem.
    If multiple targets are passed during the fit (y 2D), this
    is a 2D array of shape (n_targets, n_features), while if only
    one target is passed, this is a 1D array of length 
     n_features.
    
    intercept_ : array
    Independent term in the linear model.
    
    Notes
    -----
    From the implementation point of view, this is just plain 
     Ordinary
    Least Squares (scipy.linalg.lstsq) wrapped as a predictor 
     object.
    
    """
    def __init__(self, fit_intercept=True, normalize=False, 
     copy_X=True, 
        n_jobs=1):
        self.fit_intercept = fit_intercept
        self.normalize = normalize
        self.copy_X = copy_X
        self.n_jobs = n_jobs
    
    def fit(self, X, y, sample_weight=None):
        """
        Fit linear model.
        Parameters
        ----------
        X : numpy array or sparse matrix of shape [n_samples,
         n_features]
            Training data
        y : numpy array of shape [n_samples, n_targets]
            Target values. Will be cast to X's dtype if necessary
        sample_weight : numpy array of shape [n_samples]
            Individual weights for each sample
            .. versionadded:: 0.17
               parameter *sample_weight* support to 
                LinearRegression.
        Returns
        -------
        self : returns an instance of self.
        """
        n_jobs_ = self.n_jobs
        X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], 
            y_numeric=True, multi_output=True)
        if sample_weight is not None and np.atleast_1d
         (sample_weight).ndim > 1:
            raise ValueError("Sample weights must be 1D array 
             or scalar")
        X, y, X_offset, y_offset, X_scale = self._preprocess_data(
            X, y, fit_intercept=self.fit_intercept, normalize=self.
             normalize, 
            copy=self.copy_X, sample_weight=sample_weight)
        if sample_weight is not None:
             Sample weight can be implemented via a simple 
             rescaling.
            X, y = _rescale_data(X, y, sample_weight)
        if sp.issparse(X):
            if y.ndim < 2:
                out = sparse_lsqr(X, y)
                self.coef_ = out[0]
                self._residues = out[3]
            else:
             sparse_lstsq cannot handle y with shape (M, K)
                outs = Parallel(n_jobs=n_jobs_)(
                    delayed(sparse_lsqr)(X, :j]ravel()) for y[.
                    j in range(y.shape[1]))
                self.coef_ = np.vstack(out[0] for out in outs)
                self._residues = np.vstack(out[3] for out in outs)
        else:
            self.coef_, self._residues, self.rank_, self.singular_ = 
             linalg.lstsq(X, y)
            self.coef_ = self.coef_.T
        if y.ndim == 1:
            self.coef_ = np.ravel(self.coef_)
        self._set_intercept(X_offset, y_offset, X_scale)
        return self