Python Hinge Beispiele

Beispiel #1

Datei: plot_sgd_loss_functions.py Projekt: shirareznik/scikit-learn

SGD: Convex Loss Functions
==========================

Plot the convex loss functions supported by `sklearn.linear_model.stochastic_gradient`.
"""
print __doc__

import numpy as np
import pylab as pl
from sklearn.linear_model.sgd_fast import Hinge, \
     ModifiedHuber, SquaredLoss

###############################################################################
# Define loss funcitons
xmin, xmax = -3, 3
hinge = Hinge()
log_loss = lambda z, p: np.log2(1.0 + np.exp(-z))
modified_huber = ModifiedHuber()
squared_loss = SquaredLoss()

###############################################################################
# Plot loss funcitons
xx = np.linspace(xmin, xmax, 100)
pl.plot([xmin, 0, 0, xmax], [1, 1, 0, 0], 'k-', label="Zero-one loss")
pl.plot(xx, [hinge.loss(x, 1) for x in xx], 'g-', label="Hinge loss")
pl.plot(xx, [log_loss(x, 1) for x in xx], 'r-', label="Log loss")
pl.plot(xx, [modified_huber.loss(x, 1) for x in xx],
        'y-',
        label="Modified huber loss")
#pl.plot(xx, [2.0*squared_loss.loss(x,1) for x in xx], 'c-',
#        label="Squared loss")

Beispiel #2

Datei: plot_sgd_loss_functions.py Projekt: Scott-Alex/scikit-learn

==========================

Plot the convex loss functions supported by
`sklearn.linear_model.stochastic_gradient`.
"""
print __doc__

import numpy as np
import pylab as pl
from sklearn.linear_model.sgd_fast import Hinge, \
     ModifiedHuber, SquaredLoss

###############################################################################
# Define loss funcitons
xmin, xmax = -3, 3
hinge = Hinge()
log_loss = lambda z, p: np.log2(1.0 + np.exp(-z))
modified_huber = ModifiedHuber()
squared_loss = SquaredLoss()

###############################################################################
# Plot loss funcitons
xx = np.linspace(xmin, xmax, 100)
pl.plot([xmin, 0, 0, xmax], [1, 1, 0, 0], 'k-',
        label="Zero-one loss")
pl.plot(xx, [hinge.loss(x, 1) for x in xx], 'g-',
        label="Hinge loss")
pl.plot(xx, [log_loss(x, 1) for x in xx], 'r-',
        label="Log loss")
pl.plot(xx, [modified_huber.loss(x, 1) for x in xx], 'y-',
        label="Modified huber loss")

Beispiel #3

Datei: plot_sgd_loss_functions.py Projekt: zhisheng/scikit-learn

Plot the convex loss functions supported by
`sklearn.linear_model.stochastic_gradient`.
"""
print(__doc__)

import numpy as np
import pylab as pl
from sklearn.linear_model.sgd_fast import SquaredHinge
from sklearn.linear_model.sgd_fast import Hinge
from sklearn.linear_model.sgd_fast import ModifiedHuber
from sklearn.linear_model.sgd_fast import SquaredLoss

###############################################################################
# Define loss functions
xmin, xmax = -4, 4
hinge = Hinge(1)
squared_hinge = SquaredHinge()
perceptron = Hinge(0)
log_loss = lambda z, p: np.log2(1.0 + np.exp(-z))
modified_huber = ModifiedHuber()
squared_loss = SquaredLoss()

###############################################################################
# Plot loss funcitons
xx = np.linspace(xmin, xmax, 100)
pl.plot([xmin, 0, 0, xmax], [1, 1, 0, 0], 'k-', label="Zero-one loss")
pl.plot(xx, [hinge.loss(x, 1) for x in xx], 'g-', label="Hinge loss")
pl.plot(xx, [perceptron.loss(x, 1) for x in xx], 'm-', label="Perceptron loss")
pl.plot(xx, [log_loss(x, 1) for x in xx], 'r-', label="Log loss")
#pl.plot(xx, [2 * squared_loss.loss(x, 1) for x in xx], 'c-',
#        label="Squared loss")

Beispiel #4

Datei: plot_sgd_loss_functions.py Projekt: 2011200799/scikit-learn

All of the above loss functions are supported by
:class:`sklearn.linear_model.stochastic_gradient` .
"""
print(__doc__)

import numpy as np
import pylab as pl
from sklearn.linear_model.sgd_fast import SquaredHinge
from sklearn.linear_model.sgd_fast import Hinge
from sklearn.linear_model.sgd_fast import ModifiedHuber
from sklearn.linear_model.sgd_fast import SquaredLoss

###############################################################################
# Define loss functions
xmin, xmax = -4, 4
hinge = Hinge(1)
squared_hinge = SquaredHinge()
perceptron = Hinge(0)
log_loss = lambda z, p: np.log2(1.0 + np.exp(-z))
modified_huber = ModifiedHuber()
squared_loss = SquaredLoss()


###############################################################################
# Plot loss funcitons
xx = np.linspace(xmin, xmax, 100)
pl.plot([xmin, 0, 0, xmax], [1, 1, 0, 0], 'k-',
        label="Zero-one loss")
pl.plot(xx, [hinge.loss(x, 1) for x in xx], 'g-',
        label="Hinge loss")
pl.plot(xx, [perceptron.loss(x, 1) for x in xx], 'm-',

Beispiel #5

Datei: plot_sgd_loss_functions.py Projekt: tnunes/scikit-learn

Plot the convex loss functions supported by
`sklearn.linear_model.stochastic_gradient`.
"""
print __doc__

import numpy as np
import pylab as pl
from sklearn.linear_model.sgd_fast import SquaredHinge
from sklearn.linear_model.sgd_fast import Hinge
from sklearn.linear_model.sgd_fast import ModifiedHuber
from sklearn.linear_model.sgd_fast import SquaredLoss

###############################################################################
# Define loss functions
xmin, xmax = -4, 4
hinge = Hinge(1)
squared_hinge = SquaredHinge()
perceptron = Hinge(0)
log_loss = lambda z, p: np.log2(1.0 + np.exp(-z))
modified_huber = ModifiedHuber()
squared_loss = SquaredLoss()


###############################################################################
# Plot loss funcitons
xx = np.linspace(xmin, xmax, 100)
pl.plot([xmin, 0, 0, xmax], [1, 1, 0, 0], 'k-',
        label="Zero-one loss")
pl.plot(xx, [hinge.loss(x, 1) for x in xx], 'g-',
        label="Hinge loss")
pl.plot(xx, [perceptron.loss(x, 1) for x in xx], 'm-',