Python LogisticRegression Examples

Example #1

File: log_reg.py Project: neuronalX/esn-lm

import numpy as np
import matplotlib.pyplot as plt
from esnlm.readouts import LogisticRegression

input_dim, output_dim = 2, 2
x = np.random.rand(300, input_dim - 1)
x = np.hstack([x, np.ones((x.shape[0], 1))])
dmodel = LogisticRegression(input_dim, output_dim)
pyr = dmodel.py_given_x(x)
y = dmodel.sample_y_given_x(x)

model = LogisticRegression(input_dim, output_dim)
model.fit(x, y, method='Newton-Raphson', nb_iter=20)
py = model.py_given_x(x)
plt.plot(x[:, 0], pyr[:, 0], 'x', color='black')
plt.plot(x[:, 0], pyr[:, 1], 'x', color='black')
plt.plot(x[:, 0], py[:, 0], 'xb')
plt.plot(x[:, 0], py[:, 1], 'xg')
plt.show()

Example #2

File: log_reg.py Project: zermelozf/esn-lm

import numpy as np
import matplotlib.pyplot as plt
from esnlm.readouts import LogisticRegression

input_dim, output_dim = 2, 2
x = np.random.rand(300, input_dim-1)
x = np.hstack([x, np.ones((x.shape[0], 1))])
dmodel = LogisticRegression(input_dim, output_dim)
pyr = dmodel.py_given_x(x)
y = dmodel.sample_y_given_x(x)
 
model = LogisticRegression(input_dim, output_dim)
model.fit(x,y, method='Newton-Raphson', nb_iter=20)
py = model.py_given_x(x)
plt.plot( x[:,0], pyr[:,0], 'x', color='black')
plt.plot( x[:,0], pyr[:,1], 'x', color='black')
plt.plot( x[:,0], py[:,0], 'xb')
plt.plot( x[:,0], py[:,1], 'xg')
plt.show()

Example #3

File: logistic_regression.py Project: zermelozf/esn-lm

import numpy as np
from esnlm.readouts import LogisticRegression

# Test Model
idim, odim = 1, 3
dm =LogisticRegression(input_dim=idim+1, output_dim=odim)
x = np.random.rand(50000, idim)
x = np.hstack([x,np.ones((x.shape[0], 1))])
y = dm.sample_y_given_x(x)
print "input_dim =", x.shape[1], ", output_dim =", y.shape[1], "nb_samples:", x.shape[0]


input_dim, output_dim = x.shape[1], y.shape[1]
m = LogisticRegression(input_dim, output_dim)
init_value = m.log_likelihood(x, y)
       
initial_params = np.array(m.params)
m.log_likelihood(x, y)
method = 'CG'
print "... training using", method, ":",
m.fit(x, y, method=method)

print ""
print "Model Ini ->", init_value
print "Model Fin ->", m.log_likelihood(x, y) 
print "Model Rea ->", dm.log_likelihood(x, y)

##############
#### PLOT ####
##############
from matplotlib.pyplot import plot, get_cmap, title, show

Example #4

import numpy as np
from esnlm.readouts import LogisticRegression

# Test Model
idim, odim = 1, 3
dm = LogisticRegression(input_dim=idim + 1, output_dim=odim)
x = np.random.rand(50000, idim)
x = np.hstack([x, np.ones((x.shape[0], 1))])
y = dm.sample_y_given_x(x)
print "input_dim =", x.shape[1], ", output_dim =", y.shape[
    1], "nb_samples:", x.shape[0]

input_dim, output_dim = x.shape[1], y.shape[1]
m = LogisticRegression(input_dim, output_dim)
init_value = m.log_likelihood(x, y)

initial_params = np.array(m.params)
m.log_likelihood(x, y)
method = 'CG'
print "... training using", method, ":",
m.fit(x, y, method=method)

print ""
print "Model Ini ->", init_value
print "Model Fin ->", m.log_likelihood(x, y)
print "Model Rea ->", dm.log_likelihood(x, y)

##############
#### PLOT ####
##############
from matplotlib.pyplot import plot, get_cmap, title, show