本文整理汇总了Python中sklearn.cross_decomposition.PLSRegression.fit_transform方法的典型用法代码示例。如果您正苦于以下问题：Python PLSRegression.fit_transform方法的具体用法？Python PLSRegression.fit_transform怎么用？Python PLSRegression.fit_transform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.cross_decomposition.PLSRegression的用法示例。
在下文中一共展示了PLSRegression.fit_transform方法的3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: PLS
# 需要导入模块: from sklearn.cross_decomposition import PLSRegression [as 别名]
# 或者: from sklearn.cross_decomposition.PLSRegression import fit_transform [as 别名]
ax.scatter(X_r[y == i, 0], X_r[y == i, 1], X_r[y == i, 2], c=c)
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.axis('equal')
ax.set_xlim([-1000,4000])
ax.set_ylim([-1000,4000])
ax.set_zlim([-1000,4000])
plt.show()
# part b
PLS1 = PLS(n_components=3)
number_map = {"M": 0,"B": 1}
numeric_y = np.array(map(lambda x : number_map[x],y))
result = PLS1.fit_transform(x,numeric_y)
X_r = result[0]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for c, i, target_name in zip("rb", target_names, target_names):
ax.scatter(X_r[y == i, 0], X_r[y == i, 1], X_r[y == i, 2], c=c)
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.axis('equal')
plt.show()
validation = data[:100]
test = data[100:200]
train = data[200:]示例2: LinearSVC
# 需要导入模块: from sklearn.cross_decomposition import PLSRegression [as 别名]
# 或者: from sklearn.cross_decomposition.PLSRegression import fit_transform [as 别名]
# Make predictions using an SVM with PCA and PLS
pca_error = 0
pls_error = 0
n_folds = 10
svc = LinearSVC()
for train_inds, test_inds in KFold(X.shape[0], n_folds=n_folds):
X_train, X_test = X[train_inds], X[test_inds]
y_train, y_test = y[train_inds], y[test_inds]
# Use PCA and then classify using an SVM
X_train2 = pca.fit_transform(X_train)
X_test2 = pca.transform(X_test)
svc.fit(X_train2, y_train)
y_pred = svc.predict(X_test2)
pca_error += zero_one_loss(y_test, y_pred)
# Use PLS and then classify using an SVM
X_train2, y_train2 = pls.fit_transform(X_train, y_train)
X_test2 = pls.transform(X_test)
svc.fit(X_train2, y_train)
y_pred = svc.predict(X_test2)
pls_error += zero_one_loss(y_test, y_pred)
print(pca_error / n_folds)
print(pls_error / n_folds)
示例3: zip
# 需要导入模块: from sklearn.cross_decomposition import PLSRegression [as 别名]
# 或者: from sklearn.cross_decomposition.PLSRegression import fit_transform [as 别名]
plt.figure()
for c, i, target_name in zip("rgb", ["Iris-setosa", "Iris-versicolor", "Iris-virginica"], target_names):
plt.scatter(X_r[y == i, 0], X_r[y == i, 1], c=c, label=target_name)
plt.legend()
plt.title('PCA of IRIS dataset')
plt.axis('equal')
plt.show()
# PLS1
PLS1 = PLS(n_components=2)
X = df.as_matrix()[:, :4]
y = np.array(map(lambda x : number_map[x],df.as_matrix()[:, 4]))
string_map = {-1.2206555615733703 : "Iris-setosa", 0 : "Iris-versicolor", 1.2206555615733703 : "Iris-virginica"}
result = PLS1.fit_transform(X,y)
y = np.array(map(lambda x : string_map[x],result[1]))
target_names = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
for c, i, target_name in zip("rgb", ["Iris-setosa", "Iris-versicolor", "Iris-virginica"], target_names):
plt.scatter(result[0][y == i, 0],result[0][y == i, 1], c=c, label=target_name)
plt.legend()
plt.title('PLS1 of IRIS dataset')
plt.axis('equal')
plt.show()
# PLS2
PLS2 = PLS(n_components=2)
X = df.as_matrix()[:, :4]
y = np.array(map(lambda x : number_map[x],df.as_matrix()[:, 4]))
one_hot_y = np.zeros((len(y),3))