Esempi in Python per Lasso

Esempio n. 1

File: lasso_dense_vs_sparse_data.py Progetto: AnneLaureF/scikit-learn

from scikits.learn.linear_model.sparse import Lasso as SparseLasso
from scikits.learn.linear_model import Lasso as DenseLasso


###############################################################################
# The two Lasso implementations on Dense data
print "--- Dense matrices"

n_samples, n_features = 200, 10000
np.random.seed(0)
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)

alpha = 1
sparse_lasso = SparseLasso(alpha=alpha, fit_intercept=False)
dense_lasso = DenseLasso(alpha=alpha, fit_intercept=False)

t0 = time()
sparse_lasso.fit(X, y, maxit=1000)
print "Sparse Lasso done in %fs" % (time() - t0)

t0 = time()
dense_lasso.fit(X, y, maxit=1000)
print "Dense Lasso done in %fs" % (time() - t0)

print "Distance between coefficients : %s" % linalg.norm(sparse_lasso.coef_
                                                        - dense_lasso.coef_)

###############################################################################
# The two Lasso implementations on Sparse data

Esempio n. 2

from scipy import linalg

from scikits.learn.linear_model.sparse import Lasso as SparseLasso
from scikits.learn.linear_model import Lasso as DenseLasso

###############################################################################
# The two Lasso implementations on Dense data
print "--- Dense matrices"

n_samples, n_features = 200, 10000
np.random.seed(0)
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)

alpha = 1
sparse_lasso = SparseLasso(alpha=alpha, fit_intercept=False)
dense_lasso = DenseLasso(alpha=alpha, fit_intercept=False)

t0 = time()
sparse_lasso.fit(X, y, max_iter=1000)
print "Sparse Lasso done in %fs" % (time() - t0)

t0 = time()
dense_lasso.fit(X, y, max_iter=1000)
print "Dense Lasso done in %fs" % (time() - t0)

print "Distance between coefficients : %s" % linalg.norm(sparse_lasso.coef_ -
                                                         dense_lasso.coef_)

###############################################################################
# The two Lasso implementations on Sparse data

Esempio n. 3

File: lasso_dense_vs_sparse_data.py Progetto: AndreaCensi/scikit-learn

from scikits.learn.linear_model.sparse import Lasso as SparseLasso
from scikits.learn.linear_model import Lasso as DenseLasso


###############################################################################
# The two Lasso implementations on Dense data
print "--- Dense matrices"

n_samples, n_features = 200, 10000
np.random.seed(0)
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)

alpha = 1
sparse_lasso = SparseLasso(alpha=alpha, fit_intercept=False)
dense_lasso = DenseLasso(alpha=alpha, fit_intercept=False)

t0 = time()
sparse_lasso.fit(X, y, max_iter=1000)
print "Sparse Lasso done in %fs" % (time() - t0)

t0 = time()
dense_lasso.fit(X, y, max_iter=1000)
print "Dense Lasso done in %fs" % (time() - t0)

print "Distance between coefficients : %s" % linalg.norm(sparse_lasso.coef_
                                                        - dense_lasso.coef_)

###############################################################################
# The two Lasso implementations on Sparse data