本文整理汇总了Python中sklearn.preprocessing.MinMaxScaler.to_csv方法的典型用法代码示例。如果您正苦于以下问题：Python MinMaxScaler.to_csv方法的具体用法？Python MinMaxScaler.to_csv怎么用？Python MinMaxScaler.to_csv使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.preprocessing.MinMaxScaler的用法示例。
在下文中一共展示了MinMaxScaler.to_csv方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from sklearn.preprocessing import MinMaxScaler [as 别名]
# 或者: from sklearn.preprocessing.MinMaxScaler import to_csv [as 别名]
def main():
ska_list = open_query("SkillsKnowledgeAbilities")
data_list = pd.DataFrame([item[3:] for item in ska_list])
#STANDARD SCALE
data_list_std = StandardScaler().fit_transform(data_list)
std_df = pd.DataFrame(data_list_std, columns=SKA_LIST, index=[(item[:3]) for item in ska_list])
std_df.to_csv("PCA_Standardized.csv")
cov_mat = np.cov(data_list_std.T)
eig_vals, eig_vecs = np.linalg.eig(cov_mat)
eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:, i]) for i in range(len(eig_vals))]
print('Eigenvalues in descending order:')
for i in eig_pairs:
print(i[0])
print ""
pca_std = PCA(n_components=25)
pca_std.fit_transform(data_list_std)
for entry in pca_std.explained_variance_ratio_:
print entry
identity = np.identity(data_list.shape[1])
coef = pca_std.transform(identity)
coef_df = pd.DataFrame(coef, columns=['PC_1',
'PC_2',
'PC_3',
'PC_4',
'PC_5',
'PC_6',
'PC_7',
'PC_8',
'PC_9',
'PC_10',
'PC_11',
'PC_12',
'PC_13',
'PC_14',
'PC_15',
'PC_16',
'PC_17',
'PC_18',
'PC_19',
'PC_20',
'PC_21',
'PC_22',
'PC_23',
'PC_24',
'PC_25'
], index=SKA_LIST)
print coef_df
coef_df.to_csv("SKA_PCA.csv")
#TODO ADD THE TRANSFORMATIONS TO THE PROTOTYPE - USE AT LEAST FIRST 8 PRINCIPLE COMPONENTS OR SO
#TODO CORRELATION MATRIX
dot_product = std_df.dot(coef_df)
# normalized_dot_product = Normalizer().fit_transform(dot_product)
# normalized_dot_product = normalize(axis=0).fit_transform(dot_product)
normalized_dot_product = MinMaxScaler().fit_transform(dot_product)
normalized_dot_product = pd.DataFrame(normalized_dot_product, columns=list(dot_product), index=dot_product.index)
print normalized_dot_product
normalized_dot_product.to_csv("PC_by_job.csv")