第三重境界便是Python实现主流机器学习模型。今天向大家推荐普林斯顿博士后 David Bourgin 最近开源的项目：用 NumPy 手写所有主流 ML 模型，看了一下，代码可读性极强。
在每一个代码集下，作者都会提供不同实现的参考资料，例如模型的效果示例图、参考论文和参考链接等。
以线性回归为例，作者不但用500行代码实现了OLS/Ridge/Logistic/Bayesian linear regression
import numpy as np from ..utils.testing import is_symmetric_positive_definite, is_number class LinearRegression: def __init__(self, fit_intercept=True): """ An ordinary least squares regression model fit via the normal equation. Parameters fit_intercept : bool Whether to fit an additional intercept term in addition to the model coefficients. Default is True. """ self.beta = None self.fit_intercept = fit_intercept def fit(self, X, y): """ Fit the regression coefficients via maximum likelihood. Parameters ---------- X : :py:class:`ndarray <numpy.ndarray>` of shape `(N, M)` A dataset consisting of `N` examples, each of dimension `M`. y : :py:class:`ndarray <numpy.ndarray>` of shape `(N, K)` The targets for each of the `N` examples in `X`, where each target has dimension `K`. """ # convert X to a design matrix if we're fitting an intercept if self.fit_intercept: X = np.c_[np.ones(X.shape[0]), X] pseudo_inverse = np.dot(np.linalg.inv(np.dot(X.T, X)), X.T) self.beta = np.dot(pseudo_inverse, y) def predict(self, X): """ Used the trained model to generate predictions on a new collection of data points. Parameters ---------- X : :py:class:`ndarray <numpy.ndarray>` of shape `(Z, M)` A dataset consisting of `Z` new examples, each of dimension `M`. Returns ------- y_pred : :py:class:`ndarray <numpy.ndarray>` of shape `(Z, K)` The model predictions for the items in `X`. """ # convert X to a design matrix if we're fitting an intercept if self.fit_intercept: X = np.c_[np.ones(X.shape[0]), X] return np.dot(X, self.beta)
还画出了手写与调用sklearn的对比:
更多精彩内容，值得大家仔细挖掘，相信跟着完整实现一遍之后，大家对机器学习基础的掌握也将极其牢固。另外，建议大家配合作者提供的documentation 一同食用，效果更佳。
