numpy.zeros() numpy.arange() numpy.ones() numpy.sqrt() numpy.sum() numpy.mean() numpy.linspace() numpy.max() matplotlib.pyplot.show() matplotlib.pyplot.title() matplotlib.pyplot.ylabel() matplotlib.pyplot.xlabel() matplotlib.pyplot.plot() matplotlib.pyplot.legend() matpl...
print('the mean sqare error:%.2f'%np.mean((regr.predict(diabetes_X_test)-diabetes_y_test)**2)) print('Variance score:%.2f'%regr.score(diabetes_X_test,diabetes_y_test)) ##散点图 plt.scatter(diabetes_X_test,diabetes_y_test,color='black') plt.plot(diabetes_X_test,regr.predict(diab...
Line Regression Example DataBase:diavetes """ import matplotlib.pyplot as plt import numpy as np from sklearn import datasets,linear_model import time a=time.time() ###加载数据集 diabetes=datasets.load_diabetes() ###仅仅使用一个特征: diabetes_X=diabetes.data[:,np.newaxis,2] ###s数据划分...
Depicted are the first two principal components, explaining > 75% of the variance in the data. Data was power-transformed and standardized before the analysis. B Pair-plot, depicting the pairwise Spearman correlation between each pair of samples, and a histogram of normalized gene expression...
fluid as fluid import numpy import math import sys We introduced the dataset UCI Housing dataset via the uci_housing module It is encapsulated in the uci_housing module: The process of data download. The download data is saved in ~/.cache/paddle/dataset/uci_housing/housing.data. The process...
The scatter plot below shows the result of price prediction for parts of house with model. Each point on x-axis represents the median of the real price of the same type of house, and the y-axis represents the result of the linear regression model based on the feature prediction. When ...
prediction_variance_degree2.py robust_regression.py run_opt_to_find_best_number_of_line_segments.py sinWaveFit.png sinWaveFit16.png sineWave.py sineWave_custom_opt_bounds.py sineWave_degrees.py sineWave_time_compare.py slope_constraint_demo.ipynb ...