手把手教你Python實現30 個主流機器學習演算法
- 2019 年 10 月 4 日
- 筆記
上周推了一篇關於機器學習演算法需要掌握到什麼程度的文章
第三重境界便是Python實現主流機器學習模型。
今天向大家推薦普林斯頓博士後 David Bourgin 最近開源的項目：用 NumPy 手寫所有主流 ML 模型，看了一下，程式碼可讀性極強。
在每一個程式碼集下，作者都會提供不同實現的參考資料，例如模型的效果示例圖、參考論文和參考鏈接等。
以線性回歸為例，作者不但用500行程式碼實現了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[]), 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[]), X] return np.dot(X, self.beta)
還畫出了手寫與調用sklearn的對比:
更多精彩內容，值得大家仔細挖掘，相信跟著完整實現一遍之後，大家對機器學習基礎的掌握也將極其牢固。另外，建議大家配合作者提供的documentation 一同食用，效果更佳。
項目地址：https://github.com/ddbourgin/numpy-ml
文檔地址：https://numpy-ml.readthedocs.io/
