Beispiel #1
0
def l1_fista(t, Y, bound, Nz, alpha, iterations=5000):

    h = np.log(bound[1] / bound[0]) / (Nz - 1)  # equally spaced on logscale
    z = bound[0] * np.exp(np.arange(Nz) * h)  # z (Nz by 1)

    # construct coefficients matrix C by integral discretization (trapzoidal)
    # ||F - K*f||^2 = || F - \int_lb^ub [f(z)*z]*exp(-z*t) d(lnz) ||^2
    z_mesh, t_mesh = np.meshgrid(z, t)
    K = np.exp(-t_mesh * z_mesh)  # specify integral kernel
    K[:, 0] /= 2.
    K[:, -1] /= 2.
    K *= h

    fista = Fista(loss='least-square',
                  penalty='l11',
                  lambda_=alpha * 1e-4,
                  n_iter=5000)
    fista.fit(K, Y)

    return z, fista.coefs_, fista.predict(K)
Beispiel #2
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from fista import Fista
import numpy as np

fista = Fista(lambda_=0.5, loss='squared-hinge', penalty='l11', n_iter=10000)
X = np.random.normal(size=(10, 40))
y = np.sign(np.random.normal(size=10))
fista.fit(X, y, verbose=1)
#print "taux de bonne prediction with l11: %f " % fista.score(X, y)
#fista.penalty='l12'
#fista.fit(X, y)
#print "taux de bonne prediction with l12: %f " % fista.score(X, y)
#fista.penalty='l21'
#fista.fit(X, y)
#print "taux de bonne prediction with l21: %f " % fista.score(X, y)
#fista.penalty='l22'
#fista.fit(X, y, verbose=1)
#print "taux de bonne prediction with l22: %f " % fista.score(X, y)
#
Beispiel #3
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y = data.target
X = data.data

X = X[y < 2]
y = y[y < 2]
y[y == 0] = -1

K1 = X[:, 0]
K2 = X[:, 1]
K3 = X[:, 2]
K4 = X[:, 3]

K1 = np.dot(K1[:, np.newaxis], K1[:, np.newaxis].transpose())
K2 = np.dot(K2[:, np.newaxis], K2[:, np.newaxis].transpose())
K3 = np.dot(K3[:, np.newaxis], K3[:, np.newaxis].transpose())
K4 = np.dot(K4[:, np.newaxis], K4[:, np.newaxis].transpose())
K = np.concatenate((K1, K2, K3, K4), axis=1)

fista = Fista(loss='squared-hinge', penalty='l11', lambda_=0.1, n_iter=50)
fista.fit(K, y)
print("pourcentage de bonne prediction avec l11: %d " % fista.score(K, y))
fista.penalty = 'l12'
fista.fit(K, y)
print("pourcentage de bonne prediction avec l12: %d " % fista.score(K, y))
fista.penalty = 'l21'
fista.fit(K, y)
print("pourcentage de bonne prediction avec l21: %d " % fista.score(K, y))
fista.penalty = 'l22'
fista.fit(K, y)
print("pourcentage de bonne prediction avec l22: %d " % fista.score(K, y))
Beispiel #4
0
X = data.data

X = X[y<2]
y = y[y<2]
y[y==0] = -1

K1 = X[:, 0]
K2 = X[:, 1]
K3 = X[:, 2]
K4 = X[:, 3]

K1 = np.dot(K1[:, np.newaxis], K1[:, np.newaxis].transpose())
K2 = np.dot(K2[:, np.newaxis], K2[:, np.newaxis].transpose())
K3 = np.dot(K3[:, np.newaxis], K3[:, np.newaxis].transpose())
K4 = np.dot(K4[:, np.newaxis], K4[:, np.newaxis].transpose())
K = np.concatenate((K1, K2, K3, K4), axis=1)

fista = Fista(loss='squared-hinge', penalty='l11', lambda_=0.1, n_iter=50)
fista.fit(K, y)
print "pourcentage de bonne prediction avec l11: %d " % fista.score(K, y)
fista.penalty='l12'
fista.fit(K, y)
print "pourcentage de bonne prediction avec l12: %d " % fista.score(K, y)
fista.penalty='l21'
fista.fit(K, y)
print "pourcentage de bonne prediction avec l21: %d " % fista.score(K, y)
fista.penalty='l22'
fista.fit(K, y)
print "pourcentage de bonne prediction avec l22: %d " % fista.score(K, y)