#!/usr/bin/python
# -*- coding: utf-8 -*-

"""
=====================================================================
Example of a Multiple Layer Classifier using the Iris Dataset
=====================================================================

Based on https://scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html
"""
print(__doc__)

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn_rvm import EMRVC


def make_meshgrid(x, y, h=.02):
    """Create a mesh of points to plot in

    Parameters
    ----------
    x: data to base x-axis meshgrid on
    y: data to base y-axis meshgrid on
    h: stepsize for meshgrid, optional

    Returns
    -------
    xx, yy : ndarray
    """
    x_min, x_max = x.min() - 1, x.max() + 1
    y_min, y_max = y.min() - 1, y.max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    return xx, yy


def plot_contours(ax, clf, xx, yy, **params):
    """Plot the decision boundaries for a classifier.

    Parameters
    ----------
    ax: matplotlib axes object
    clf: a classifier
    xx: meshgrid ndarray
    yy: meshgrid ndarray
    params: dictionary of params to pass to contourf, optional
    """
    #Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    out = ax.contourf(xx, yy, Z, **params)
    return out

# import some data to play with
iris = datasets.load_iris()
# Take the first two features.
X = iris.data[:, :2]
y = iris.target


models = (EMRVC(kernel='linear'),
          EMRVC(kernel='rbf'),
          EMRVC(kernel='sigmoid'))
models = (clf.fit(X, y) for clf in models)

# title for the plots
titles = ('RVC with linear kernel',
          'RVC with RBF kernel')

# Set-up 2x2 grid for plotting.
fig, sub = plt.subplots(1, 2)

plt.subplots_adjust(wspace=0.4, hspace=0.4)
X0, X1 = X[:, 0], X[:, 1]
xx, yy = make_meshgrid(X0, X1)

for clf, title, ax in zip(models, titles, sub.flatten()):
    plot_contours(ax, clf, xx, yy,
                  cmap=plt.cm.coolwarm, alpha=0.8)
    ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
    #ax.scatter(clf.relevance_vectors_[:, 0], clf.relevance_vectors_[:, 1], s=100, facecolor="none", edgecolor="g")
    #ax.colorbar()
    ax.set_xlim(xx.min(), xx.max())
    ax.set_ylim(yy.min(), yy.max())
    ax.set_xlabel('Sepal length')
    ax.set_ylabel('Sepal width')
    ax.set_xticks(())
    ax.set_yticks(())
    ax.set_title(title)

plt.show()
#!/usr/bin/python
# -*- coding: utf-8 -*-

"""
=====================================================================
Example of a Multiple Layer Classifier using the Iris Dataset
=====================================================================

Based on https://scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html
"""
print(__doc__)

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn_rvm import EMRVC


def make_meshgrid(x, y, h=.02):
    """Create a mesh of points to plot in

    Parameters
    ----------
    x: data to base x-axis meshgrid on
    y: data to base y-axis meshgrid on
    h: stepsize for meshgrid, optional

    Returns
    -------
    xx, yy : ndarray
    """
    x_min, x_max = x.min() - 1, x.max() + 1
    y_min, y_max = y.min() - 1, y.max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    return xx, yy


def plot_contours(ax, clf, xx, yy, **params):
    """Plot the decision boundaries for a classifier.

    Parameters
    ----------
    ax: matplotlib axes object
    clf: a classifier
    xx: meshgrid ndarray
    yy: meshgrid ndarray
    params: dictionary of params to pass to contourf, optional
    """
    #Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    out = ax.contourf(xx, yy, Z, **params)
    return out

# import some data to play with
iris = datasets.load_iris()
# Take the first two features.
X = iris.data[:, :2]
y = iris.target


models = (EMRVC(kernel='linear'),
          EMRVC(kernel='rbf'),
          EMRVC(kernel='sigmoid'))
models = (clf.fit(X, y) for clf in models)

# title for the plots
titles = ('RVC with linear kernel',
          'RVC with RBF kernel')

# Set-up 2x2 grid for plotting.
fig, sub = plt.subplots(1, 2)

plt.subplots_adjust(wspace=0.4, hspace=0.4)
X0, X1 = X[:, 0], X[:, 1]
xx, yy = make_meshgrid(X0, X1)

for clf, title, ax in zip(models, titles, sub.flatten()):
    plot_contours(ax, clf, xx, yy,
                  cmap=plt.cm.coolwarm, alpha=0.8)
    ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
    #ax.scatter(clf.relevance_vectors_[:, 0], clf.relevance_vectors_[:, 1], s=100, facecolor="none", edgecolor="g")
    #ax.colorbar()
    ax.set_xlim(xx.min(), xx.max())
    ax.set_ylim(yy.min(), yy.max())
    ax.set_xlabel('Sepal length')
    ax.set_ylabel('Sepal width')
    ax.set_xticks(())
    ax.set_yticks(())
    ax.set_title(title)

plt.show()
#!/usr/bin/python # -*- coding: utf-8 -*- ===================================================================== Example of a Multiple Layer Classifier using the Iris Dataset =====================================================================  Based on https://scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html Create a mesh of points to plot in      Parameters     ----------     x: data to base x-axis meshgrid on     y: data to base y-axis meshgrid on     h: stepsize for meshgrid, optional      Returns     -------     xx, yy : ndarray Plot the decision boundaries for a classifier.      Parameters     ----------     ax: matplotlib axes object     clf: a classifier     xx: meshgrid ndarray     yy: meshgrid ndarray     params: dictionary of params to pass to contourf, optional #Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # import some data to play with # Take the first two features. # title for the plots # Set-up 2x2 grid for plotting. #ax.scatter(clf.relevance_vectors_[:, 0], clf.relevance_vectors_[:, 1], s=100, facecolor="none", edgecolor="g") #ax.colorbar()