机器学习红酒产地预测问题代码资料.zip

import numpy as np from sklearn.datasets import fetch_mldata from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from lib.softmax_regression_sgd import SoftmaxRegression from lib.classification_metrics import accuracy_score #导入数据 from sklearn.datasets import load_wine def convert_to_vectors(c): m = len(c) k = np.max(c) + 1 y = np.zeros(m * k).reshape(m,k) for i in range(m): y[i][c[i]] = 1 return y def process_features(X): scaler = MinMaxScaler(feature_range=(0,1)) X = scaler.fit_transform(1.0 * X) m, n = X.shape X = np.c_[np.ones((m, 1)), X] return X rwine = load_wine() #导入红酒数据 m=178 n = 13 X = rwine.data y = rwine.target c = rwine["target"].astype(np.int) y = convert_to_vectors(c) print(rwine.feature_names) print(rwine.target_names) print(X.shape) print(y.shape) X_train, X_test, y_train, y_test, c_train, c_test = train_test_split(X, y, c, test_size=0.2) X_train = process_features(X_train) X_test = process_features(X_test) model = SoftmaxRegression() model.fit(X_train, y_train, eta_0=50, eta_1=100, N=5000) c_pred = model.predict(X_test) accuracy = accuracy_score(c_test, c_pred) print("accuracy = {}".format(accuracy))