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Python StandardScaler.as_matrix方法代碼示例

本文整理匯總了Python中sklearn.preprocessing.StandardScaler.as_matrix方法的典型用法代碼示例。如果您正苦於以下問題:Python StandardScaler.as_matrix方法的具體用法?Python StandardScaler.as_matrix怎麽用?Python StandardScaler.as_matrix使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在sklearn.preprocessing.StandardScaler的用法示例。


在下文中一共展示了StandardScaler.as_matrix方法的2個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: standardize

# 需要導入模塊: from sklearn.preprocessing import StandardScaler [as 別名]
# 或者: from sklearn.preprocessing.StandardScaler import as_matrix [as 別名]
def standardize(df):
    X = df.drop(['id', 'group'], axis=1)
    X.ix[X.gender == 'f', 'gender'] = 1.0
    X.ix[X.gender == 'm', 'gender'] = -1.0
    X['gender'] = pd.to_numeric(X.gender)
    X = StandardScaler().fit_transform(X.as_matrix())
    return X
開發者ID:tomhettinger,項目名稱:clustering,代碼行數:9,代碼來源:clustering.py

示例2: LabelEncoder

# 需要導入模塊: from sklearn.preprocessing import StandardScaler [as 別名]
# 或者: from sklearn.preprocessing.StandardScaler import as_matrix [as 別名]
# Encode the nta column
le = LabelEncoder()
nta_encoded = le.fit_transform(data.nta)
data['nta_encoded'] = nta_encoded

# Drop the columns we won't use for our model
data.drop(['nta','date_hour','nta_dt','nbhd_name','Unnamed: 0'],axis=1,inplace=True)

# Scale our data (not every column needs to be scaled)
to_scale = data[(data.columns-['month','day','hour','nta_encoded'])]
X = StandardScaler().fit_transform(to_scale)
X = pd.DataFrame(X)
for col in ['month','day','hour','nta_encoded']:
    X[col] = data[col]
X = X.as_matrix()

# We want to determine the number of bins we are going to classify into by using
# k-means clustering different values of n clusters. We are going to choose the
# n with the best silhouette score
cluster_scores = []
for n in range(3,6):
    # Initialize and fit a KMeans object
    k_means = cluster.KMeans(n_clusters=n,n_jobs=-1,verbose=1)
    k_means.fit(X)
    # Get the cluster labels for each point
    labels = k_means.labels_
    # Initialize a list to store this n's silhouette scores
    scores = []
    # We need to limit the sample_size when calculating silhouette scores due to
    # computation time
開發者ID:JesseBickford,項目名稱:Predicting_Uber_Demand,代碼行數:32,代碼來源:unsupervised_clustering.py


注:本文中的sklearn.preprocessing.StandardScaler.as_matrix方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台,相關代碼片段篩選自各路編程大神貢獻的開源項目,源碼版權歸原作者所有,傳播和使用請參考對應項目的License;未經允許,請勿轉載。