import numpy as np from .metrics import r2_score class SimpleLinearRegression: def __init__(self): """初始化Simple Linear Regression模型""" self.a_ = None self.b_ = None def fit(self, x_train, y_train): """根据训练数据集x_train, y_train训练Simple Linear Regression模型""" assert x_train.ndim == 1, \ "Simple Linear Regressor can only solve single feature training data." assert len(x_train) == len(y_train), \ "the size of x_train must be equal to the size of y_train" x_mean = np.mean(x_train) y_mean = np.mean(y_train) self.a_ = (x_train - x_mean).dot(y_train - y_mean) / (x_train - x_mean).dot(x_train - x_mean)#向量化 点乘运算 self.b_ = y_mean - self.a_ * x_mean return self def predict(self, x_predict): """给定待预测数据集x_predict,返回表示x_predict的结果向量""" assert x_predict.ndim == 1, \ "Simple Linear Regressor can only solve single feature training data." assert self.a_ is not None and self.b_ is not None, \ "must fit before predict!" return np.array([self._predict(x) for x in x_predict]) def _predict(self, x_single): """给定单个待预测数据x,返回x的预测结果值""" return self.a_ * x_single + self.b_ def score(self, x_test, y_test): """根据测试数据集 x_test 和 y_test 确定当前模型的准确度""" y_predict = self.predict(x_test) return r2_score(y_test, y_predict) def __repr__(self): return "SimpleLinearRegression()"
简单线性回归预测实现
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转载自www.cnblogs.com/Erick-L/p/9031064.html
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