# In[361]: metastatic_exp = pd.read_csv(os.path.join(external_data_dir, "gse18549_expression.csv"), index_col=0).T.values metastatic_exp_scaled = scale(metastatic_exp) metastatic_exp_robust = RobustScaler( quantile_range=(1.0, 99.0)).fit_transform(metastatic_exp) metastatic_exp_normed = normalize(metastatic_exp) metastatic_exp_scaled_normed = normalize(scale(metastatic_exp)) # In[362]: print(metastatic_exp.min().min(), metastatic_exp.max().max()) print(metastatic_exp_scaled.min().min(), metastatic_exp_scaled.max().max()) print(metastatic_exp_robust.min().min(), metastatic_exp_robust.max().max()) print(metastatic_exp_normed.min().min(), metastatic_exp_normed.max().max()) print(metastatic_exp_scaled_normed.min().min(), metastatic_exp_scaled_normed.max().max()) # In[366]: _ = plt.hist(metastatic_exp.flatten(), bins=50, normed=True, label='raw') _ = plt.hist(metastatic_exp_scaled.flatten(), bins=50, normed=True, label='scale') _ = plt.hist(metastatic_exp_robust.flatten(), bins=50, normed=True, label='quantile')
X_scale=minmax_scale(X) elif c==0:#不进行归一化 X_scale=X elif c==3:#鲁棒性归一化 from sklearn.preprocessing import RobustScaler X_scale=RobustScaler().fit_transform(X) print 'the standar result of X is:',X_scale ##测试X_scale,正常情况下均值为0,方差为1 #1. print 'mean=',X_scale.mean() print 'std=',X_scale.std() #2. print 'min=',X_scale.min() print 'max=',X_scale.max() csv_file1.close() ##为了理解方便、表示方法简单 X=X_scale ##归一化之后的统计信息 ##获得X的统计信息 statistics(X) ##频率分布图 #drawHist(X,'AOD','Frequency','the Frequency of standar AOD') ##频率累计图 #drawCumulativeHist(X,'AOD','Frequency','Curve cumulative of standar AOD') ##箱图 #drawBox(X.reshape(264,),'AOD','BOX of standar AOD')