Python LinearRidgeRegression.set_labels示例

示例#1

文件： regression_lars.py 项目： serialhex/shogun

    X[:,i] -= np.mean(X[:,i])
    X[:,i] /= np.linalg.norm(X[:,i])
y -= np.mean(y)

# train LASSO
LeastAngleRegression = LeastAngleRegression()
LeastAngleRegression.set_labels(Labels(y))
LeastAngleRegression.train(RealFeatures(X.T))

# train ordinary LSR
if use_ridge:
    lsr = LinearRidgeRegression(0.01, RealFeatures(X.T), Labels(y))
    lsr.train()
else:
    lsr = LeastSquaresRegression()
    lsr.set_labels(Labels(y))
    lsr.train(RealFeatures(X.T))

# gather LASSO path
path = np.zeros((p, LeastAngleRegression.get_path_size()))
for i in xrange(path.shape[1]):
    path[:,i] = LeastAngleRegression.get_w(i)

# apply on training data
mse_train = np.zeros(LeastAngleRegression.get_path_size())
for i in xrange(mse_train.shape[0]):
    LeastAngleRegression.switch_w(i)
    ypred = LeastAngleRegression.apply(RealFeatures(X.T)).get_labels()
    mse_train[i] = np.dot(ypred - y, ypred - y) / y.shape[0]
ypred = lsr.apply(RealFeatures(X.T)).get_labels()
mse_train_lsr = np.dot(ypred - y, ypred - y) / y.shape[0]

示例#2

文件： regression_lars.py 项目： vinodrajendran001/ASP

    X[:, i] -= np.mean(X[:, i])
    X[:, i] /= np.linalg.norm(X[:, i])
y -= np.mean(y)

# train LASSO
LeastAngleRegression = LeastAngleRegression()
LeastAngleRegression.set_labels(RegressionLabels(y))
LeastAngleRegression.train(RealFeatures(X.T))

# train ordinary LSR
if use_ridge:
    lsr = LinearRidgeRegression(0.01, RealFeatures(X.T), Labels(y))
    lsr.train()
else:
    lsr = LeastSquaresRegression()
    lsr.set_labels(RegressionLabels(y))
    lsr.train(RealFeatures(X.T))

# gather LASSO path
path = np.zeros((p, LeastAngleRegression.get_path_size()))
for i in xrange(path.shape[1]):
    path[:, i] = LeastAngleRegression.get_w(i)

# apply on training data
mse_train = np.zeros(LeastAngleRegression.get_path_size())
for i in xrange(mse_train.shape[0]):
    LeastAngleRegression.switch_w(i)
    ypred = LeastAngleRegression.apply(RealFeatures(X.T)).get_labels()
    mse_train[i] = np.dot(ypred - y, ypred - y) / y.shape[0]
ypred = lsr.apply(RealFeatures(X.T)).get_labels()
mse_train_lsr = np.dot(ypred - y, ypred - y) / y.shape[0]

示例#3

文件： regression_lars.py 项目： jimloco/shogun

    X[:,i] -= np.mean(X[:,i])
    X[:,i] /= np.linalg.norm(X[:,i])
y -= np.mean(y)

# train LASSO
LeastAngleRegression = LeastAngleRegression()
LeastAngleRegression.set_labels(RegressionLabels(y))
LeastAngleRegression.train(RealFeatures(X.T))

# train ordinary LSR
if use_ridge:
    lsr = LinearRidgeRegression(0.01, RealFeatures(X.T), Labels(y))
    lsr.train()
else:
    lsr = LeastSquaresRegression()
    lsr.set_labels(RegressionLabels(y))
    lsr.train(RealFeatures(X.T))

# gather LASSO path
path = np.zeros((p, LeastAngleRegression.get_path_size()))
for i in xrange(path.shape[1]):
    path[:,i] = LeastAngleRegression.get_w(i)

# apply on training data
mse_train = np.zeros(LeastAngleRegression.get_path_size())
for i in xrange(mse_train.shape[0]):
    LeastAngleRegression.switch_w(i)
    ypred = LeastAngleRegression.apply(RealFeatures(X.T)).get_labels()
    mse_train[i] = np.dot(ypred - y, ypred - y) / y.shape[0]
ypred = lsr.apply(RealFeatures(X.T)).get_labels()
mse_train_lsr = np.dot(ypred - y, ypred - y) / y.shape[0]

示例#4

文件： regression_lars.py 项目： serialhex/shogun

    X[:, i] -= np.mean(X[:, i])
    X[:, i] /= np.linalg.norm(X[:, i])
y -= np.mean(y)

# train LASSO
LeastAngleRegression = LeastAngleRegression()
LeastAngleRegression.set_labels(Labels(y))
LeastAngleRegression.train(RealFeatures(X.T))

# train ordinary LSR
if use_ridge:
    lsr = LinearRidgeRegression(0.01, RealFeatures(X.T), Labels(y))
    lsr.train()
else:
    lsr = LeastSquaresRegression()
    lsr.set_labels(Labels(y))
    lsr.train(RealFeatures(X.T))

# gather LASSO path
path = np.zeros((p, LeastAngleRegression.get_path_size()))
for i in xrange(path.shape[1]):
    path[:, i] = LeastAngleRegression.get_w(i)

# apply on training data
mse_train = np.zeros(LeastAngleRegression.get_path_size())
for i in xrange(mse_train.shape[0]):
    LeastAngleRegression.switch_w(i)
    ypred = LeastAngleRegression.apply(RealFeatures(X.T)).get_labels()
    mse_train[i] = np.dot(ypred - y, ypred - y) / y.shape[0]
ypred = lsr.apply(RealFeatures(X.T)).get_labels()
mse_train_lsr = np.dot(ypred - y, ypred - y) / y.shape[0]