fabianp

import numpy as np
from scipy import linalg, optimize

MAX_ITER = 100


def group_lasso(X, y, alpha, groups, max_iter=MAX_ITER, rtol=1e-6,
             verbose=False):
    """
    Linear least-squares with l2/l1 regularization solver.

agramfort

"""
This module implements the Lowess function for nonparametric regression.

Functions:
lowess Fit a smooth nonparametric regression curve to a scatterplot.

For more information, see

William S. Cleveland: "Robust locally weighted regression and smoothing
scatterplots", Journal of the American Statistical Association, December 1979,

asmeurer

BenoitDamota

"""
LightPCA : PCA with little memory footprint, but can only fit_transform()
the data (no transform(), no inverse_transform()).
targeted data (be sure to have at least 5-6GB of free memory):
>>> import numpy as np
>>> from light_pca import LightPCA
>>> X = np.random.randn(1301, 500000) # ~5GB
>>> pca = LightPCA(copy=False, n_components=1300)
>>> X = pca.fit_transform(X)
"""

pprett

import numpy as np
import svmlight_loader

from sklearn.ensemble import GradientBoostingRegressor

from time import time

ROOT_DIR = '/home/pprett/corpora/yahoo-ltrc-2010/data'
X_train, y_train = svmlight_loader.load_svmlight_file(ROOT_DIR + '/set1.train.txt',
                                                      n_features=700,

fabianp

import numpy as np
from scipy import linalg

def ridge(A, b, alphas):
    """Return coefficients for regularized least squares 
               ||A x - b|| + alpha ||x||
    """ 
    U, s, V = linalg.svd(X, full_matrices=False)
    d = np.dot(U.T, y) / (s + alphas[:, np.newaxis] / s)
    return np.dot(d, V)