{"id":1311,"date":"2023-03-25T10:12:45","date_gmt":"2023-03-25T02:12:45","guid":{"rendered":""},"modified":"2023-03-25T10:12:45","modified_gmt":"2023-03-25T02:12:45","slug":"\u4eba\u5de5\u667a\u80fd\u76d1\u7763\u5b66\u4e60(\u56de\u5f52)","status":"publish","type":"post","link":"https:\/\/bianchenghao6.com\/1311.html","title":{"rendered":"\u4eba\u5de5\u667a\u80fd\u76d1\u7763\u5b66\u4e60(\u56de\u5f52)"},"content":{"rendered":"


\n <\/head>
\n <\/p>\n

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\u4eba\u5de5\u667a\u80fd\u76d1\u7763\u5b66\u4e60(\u56de\u5f52)<\/h1>\n

\u4eba\u5de5\u667a\u80fd\u76d1\u7763\u5b66\u4e60(\u56de\u5f52)\u8be6\u7ec6\u64cd\u4f5c\u6559\u7a0b<\/span>\n <\/div>\n

\n \u56de\u5f52\u662f\u6700\u91cd\u8981\u7684\u7edf\u8ba1\u548c\u673a\u5668\u5b66\u4e60\u5de5\u5177\u4e4b\u4e00\u3002 \u6211\u4eec\u8ba4\u4e3a\u673a\u5668\u5b66\u4e60\u7684\u65c5\u7a0b\u4ece\u56de\u5f52\u5f00\u59cb\u5e76\u4e0d\u662f\u9519\u7684\u3002 \u5b83\u53ef\u4ee5\u88ab\u5b9a\u4e49\u4e3a\u4f7f\u6211\u4eec\u80fd\u591f\u6839\u636e\u6570\u636e\u505a\u51fa\u51b3\u5b9a\u7684\u53c2\u6570\u5316\u6280\u672f\uff0c\u6216\u8005\u6362\u8a00\u4e4b\uff0c\u5141\u8bb8\u901a\u8fc7\u5b66\u4e60\u8f93\u5165\u548c\u8f93\u51fa\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u6765\u57fa\u4e8e\u6570\u636e\u505a\u51fa\u9884\u6d4b\u3002 \u8fd9\u91cc\uff0c\u4f9d\u8d56\u4e8e\u8f93\u5165\u53d8\u91cf\u7684\u8f93\u51fa\u53d8\u91cf\u662f\u8fde\u7eed\u503c\u7684\u5b9e\u6570\u3002 \u5728\u56de\u5f52\u4e2d\uff0c\u8f93\u5165\u548c\u8f93\u51fa\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u5f88\u91cd\u8981\uff0c\u5b83\u6709\u52a9\u4e8e\u6211\u4eec\u7406\u89e3\u8f93\u51fa\u53d8\u91cf\u7684\u503c\u968f\u8f93\u5165\u53d8\u91cf\u7684\u53d8\u5316\u800c\u53d8\u5316\u3002 \u56de\u5f52\u5e38\u7528\u4e8e\u9884\u6d4b\u4ef7\u683c\uff0c\u7ecf\u6d4e\uff0c\u53d8\u5316\u7b49\u3002\n <\/div>\n

\u5728Python\u4e2d\u6784\u5efa\u56de\u5f52\u5668<\/h2>\n
\n \u5728\u672c\u8282\u4e2d\uff0c\u6211\u4eec\u5c06\u5b66\u4e60\u5982\u4f55\u6784\u5efa\u5355\u4e00\u4ee5\u53ca\u591a\u53d8\u91cf\u56de\u5f52\u5668\u3002\n <\/div>\n
\n \u7ebf\u6027\u56de\u5f52\u5668\/\u5355\u53d8\u91cf\u56de\u5f52\u5668<\/strong>\n <\/div>\n
\n \u8ba9\u6211\u4eec\u91cd\u70b9\u4ecb\u7ecd\u4e00\u4e9b\u5fc5\u9700\u7684\u8f6f\u4ef6\u5305 -\n <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
import <\/span>numpy as <\/span>np
 from <\/span>sklearn import <\/span>linear_model
 import <\/span>sklearn.metrics as <\/span>sm
 import <\/span>matplotlib.pyplot as <\/span>plt
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u73b0\u5728\uff0c\u6211\u4eec\u9700\u8981\u63d0\u4f9b\u8f93\u5165\u6570\u636e\uff0c\u5e76\u5c06\u6570\u636e\u4fdd\u5b58\u5728\u540d\u4e3alinear.txt\u7684\u6587\u4ef6\u4e2d\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
input = 'D:\/ProgramData\/linear.txt'<\/span>
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u4f7f\u7528np.loadtxt\u51fd\u6570\u52a0\u8f7d\u8fd9\u4e9b\u6570\u636e\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
input_data = np.loadtxt<\/span>(input, delimiter=','<\/span>)
 X, y = input_data[:, :-1], input_data[:, -1]
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u4e0b\u4e00\u6b65\u5c06\u662f\u57f9\u8bad\u6a21\u578b\u3002\u4e0b\u9762\u7ed9\u51fa\u57f9\u8bad\u548c\u6d4b\u8bd5\u6837\u672c\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
training_samples = int(0.6 * len<\/span>(X))
 testing_samples = len(X) - num_training
 X_train, y_train <\/span>= X[:training_samples], y[:training_samples]
 X_test, y_test = X[training_samples:], y[training_samples:]
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u73b0\u5728\uff0c\u6211\u4eec\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u7ebf\u6027\u56de\u5f52\u5668\u5bf9\u8c61\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
reg_linear = linear_model.LinearRegression<\/span>()
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u7528\u8bad\u7ec3\u6837\u672c\u8bad\u7ec3\u5bf9\u8c61\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
reg_linear.fit<\/span>(X_train, y_train)
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u4e0b\u9762\u4f7f\u7528\u6d4b\u8bd5\u6570\u636e\u505a\u9884\u6d4b\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
y_test_pred = reg_linear.predict<\/span>(X_test)
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u73b0\u5728\u7ed8\u5236\u5e76\u53ef\u89c6\u5316\u6570\u636e\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
plt.scatter<\/span>(X_test, y_test, color <\/span>= 'red'<\/span>)
 plt.plot<\/span>(X_test, y_test_pred, color <\/span>= 'black'<\/span>, linewidth = 2)
 plt.xticks<\/span>(())
 plt.yticks<\/span>(())
 plt.show<\/span>()
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u6267\u884c\u4e0a\u9762\u793a\u4f8b\u4ee3\u7801\uff0c\u8f93\u51fa\u4ee5\u4e0b\u7ed3\u679c -\n  <\/div>\n
\n   \u4eba\u5de5\u667a\u80fd\u76d1\u7763\u5b66\u4e60(\u56de\u5f52)_https:\/\/bianchenghao6.com_\u3010\u4eba\u5de5\u667a\u80fd\u6559\u7a0b\u3011_\u7b2c1\u5f20\n  <\/div>\n
\n    \u73b0\u5728\uff0c\u6211\u4eec\u53ef\u4ee5\u8ba1\u7b97\u7ebf\u6027\u56de\u5f52\u7684\u6027\u80fd\u5982\u4e0b -\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
print(\"Performance of Linear regressor:\"<\/span>)
 print(\"Mean absolute error <\/span>=\"<\/span>, round(sm.mean_absolute_error<\/span>(y_test, y_test_pred), 2))
 print(\"Mean squared error <\/span>=\"<\/span>, round(sm.mean_squared_error<\/span>(y_test, y_test_pred), 2))
 print(\"Median absolute error <\/span>=\"<\/span>, round(sm.median_absolute_error<\/span>(y_test, y_test_pred), 2))
 print(\"Explain <\/span>variance score =\"<\/span>, round(sm.explained_variance_score<\/span>(y_test, y_test_pred),
 2))
 print(\"R2 score =\"<\/span>, round(sm.r2_score<\/span>(y_test, y_test_pred), 2))
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u7ebf\u6027\u56de\u5f52\u5668\u7684\u6027\u80fd\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b -\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
Mean absolute error <\/span>= 1.78
 Mean squared error <\/span>= 3.89
 Median absolute error <\/span>= 2.01
 Explain <\/span>variance score = -0.09
 R2 score = -0.09
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86\u8fd9\u4e9b\u5c0f\u6570\u636e\u6e90\u3002 \u5982\u679c\u60f3\u8981\u5904\u7406\u4e00\u4e9b\u5927\u7684\u6570\u636e\u96c6\uff0c\u90a3\u4e48\u53ef\u4ee5\u4f7f\u7528sklearn.dataset\u6765\u5bfc\u5165\u66f4\u5927\u7684\u6570\u636e\u96c6\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
2,4.82.9,4.72.5,53.2,5.56,57.6,43.2,0.92.9,1.92.4,
 3.50.5,3.41,40.9,5.91.2,2.583.2,5.65.1,1.54.5,
 1.22.3,6.32.1,2.8
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n   \u591a\u53d8\u91cf\u56de\u5f52<\/strong>\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u5bfc\u5165\u4e00\u4e9b\u5fc5\u9700\u7684\u8f6f\u4ef6\u5305 -\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
import <\/span>numpy as <\/span>np
 from <\/span>sklearn import <\/span>linear_model
 import <\/span>sklearn.metrics as <\/span>sm
 import <\/span>matplotlib.pyplot as <\/span>plt
 from <\/span>sklearn.preprocessing import <\/span>PolynomialFeatures
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u73b0\u5728\uff0c\u9700\u8981\u63d0\u4f9b\u8f93\u5165\u6570\u636e\uff0c\u5e76\u5c06\u6570\u636e\u4fdd\u5b58\u5728\u540d\u4e3alinear.txt\u7684\u6587\u4ef6\u4e2d\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
input = 'D:\/ProgramData\/Mul_linear.txt'<\/span>
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u6211\u4eec\u5c06\u901a\u8fc7\u4f7f\u7528np.loadtxt\u51fd\u6570\u52a0\u8f7d\u8fd9\u4e9b\u6570\u636e\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
input_data = np.loadtxt<\/span>(input, delimiter=','<\/span>)
 X, y = input_data[:, :-1], input_data[:, -1]
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u4e0b\u4e00\u6b65\u5c06\u662f\u8bad\u7ec3\u6a21\u578b; \u4f1a\u63d0\u4f9b\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6837\u54c1\u6570\u636e\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
training_samples = int(0.6 * len<\/span>(X))
 testing_samples = len(X) - num_training
 X_train, y_train <\/span>= X[:training_samples], y[:training_samples]
 X_test, y_test = X[training_samples:], y[training_samples:]
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u73b0\u5728\uff0c\u6211\u4eec\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u7ebf\u6027\u56de\u5f52\u5668\u5bf9\u8c61\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
reg_linear_mul = linear_model.LinearRegression<\/span>()
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u7528\u8bad\u7ec3\u6837\u672c\u8bad\u7ec3\u5bf9\u8c61\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
reg_linear_mul.fit<\/span>(X_train, y_train)
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u73b0\u5728\uff0c\u6700\u540e\u9700\u8981\u7528\u6d4b\u8bd5\u6570\u636e\u505a\u9884\u6d4b\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
y_test_pred = reg_linear_mul.predict<\/span>(X_test)
 print(\"Performance of Linear regressor:\"<\/span>)
 print(\"Mean absolute error <\/span>=\"<\/span>, round(sm.mean_absolute_error<\/span>(y_test, y_test_pred), 2))
 print(\"Mean squared error <\/span>=\"<\/span>, round(sm.mean_squared_error<\/span>(y_test, y_test_pred), 2))
 print(\"Median absolute error <\/span>=\"<\/span>, round(sm.median_absolute_error<\/span>(y_test, y_test_pred), 2))
 print(\"Explain <\/span>variance score =\"<\/span>, round(sm.explained_variance_score<\/span>(y_test, y_test_pred), 2))
 print(\"R2 score =\"<\/span>, round(sm.r2_score<\/span>(y_test, y_test_pred), 2))
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u7ebf\u6027\u56de\u5f52\u5668\u7684\u6027\u80fd\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b -\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
Mean absolute error <\/span>= 0.6
 Mean squared error <\/span>= 0.65
 Median absolute error <\/span>= 0.41
 Explain <\/span>variance score = 0.34
 R2 score = 0.33
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u73b0\u5728\uff0c\u6211\u4eec\u5c06\u521b\u5efa\u4e00\u4e2a10\u9636\u591a\u9879\u5f0f\u5e76\u8bad\u7ec3\u56de\u5f52\u5668\u3002\u5e76\u63d0\u4f9b\u6837\u672c\u6570\u636e\u70b9\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
polynomial = PolynomialFeatures(degree = 10)
 X_train_transformed = polynomial.fit_transform<\/span>(X_train)
 datapoint = [[2.23, 1.35, 1.12]]
 poly_datapoint = polynomial.fit_transform<\/span>(datapoint)
 poly_linear_model <\/span>= linear_model.LinearRegression<\/span>()
 poly_linear_model.fit<\/span>(X_train_transformed, y_train)
 print(\"\\nLinear regression:\\n\"<\/span>, reg_linear_mul.predict<\/span>(datapoint))
 print(\"\\nPolynomial regression:\\n\"<\/span>, poly_linear_model.predict<\/span>(poly_datapoint))
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u7ebf\u6027\u56de\u5f52 -\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
[2.40170462]
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u591a\u9879\u5f0f\u56de\u5f52 -\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
[1.8697225]
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
\n    \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86\u8fd9\u4e9b\u5c0f\u6570\u636e\u3002 \u5982\u679c\u60f3\u8981\u4e00\u4e2a\u5927\u7684\u6570\u636e\u96c6\uff0c\u90a3\u4e48\u53ef\u4ee5\u4f7f\u7528sklearn.dataset\u6765\u5bfc\u5165\u4e00\u4e2a\u66f4\u5927\u7684\u6570\u636e\u96c6\u3002\n  <\/div>\n
\n
 # Filename : example.py<\/span>
# Copyright : 2020 By Lidihuo<\/span>
# Author by : www.lidihuo.com<\/span>
# Date : 2020-08-26<\/span>
2,4.8,1.2,3.22.9,4.7,1.5,3.62.5,5,2.8,23.2,5.5,3.5,2.16,5,
 2,3.27.6,4,1.2,3.23.2,0.9,2.3,1.42.9,1.9,2.3,1.22.4,3.5,
 2.8,3.60.5,3.4,1.8,2.91,4,3,2.50.9,5.9,5.6,0.81.2,2.58,
 3.45,1.233.2,5.6,2,3.25.1,1.5,1.2,1.34.5,1.2,4.1,2.32.3,
 6.3,2.5,3.22.1,2.8,1.2,3.6
 <\/span><\/code><\/pre>\n<\/p><\/div>\n
 <\/body>
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