mirror of
https://github.com/gsi-upm/sitc
synced 2024-11-24 07:22:29 +00:00
51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from matplotlib.colors import ListedColormap
|
|
from sklearn import neighbors, datasets
|
|
from sklearn.neighbors import KNeighborsClassifier
|
|
|
|
# Taken from http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html
|
|
def plot_classification_iris():
|
|
"""
|
|
Plot knn classification of the iris dataset
|
|
"""
|
|
# import some data to play with
|
|
iris = datasets.load_iris()
|
|
|
|
X = iris.data[:, :2] # we only take the first two features. We could
|
|
# avoid this ugly slicing by using a two-dim dataset
|
|
y = iris.target
|
|
|
|
h = .02 # step size in the mesh
|
|
n_neighbors = 15
|
|
|
|
# Create color maps
|
|
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
|
|
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
|
|
|
|
for weights in ['uniform', 'distance']:
|
|
# we create an instance of Neighbours Classifier and fit the data.
|
|
clf = KNeighborsClassifier(n_neighbors, weights=weights)
|
|
clf.fit(X, y)
|
|
|
|
# Plot the decision boundary. For that, we will assign a color to each
|
|
# point in the mesh [x_min, m_max]x[y_min, y_max].
|
|
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
|
|
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
|
|
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
|
|
np.arange(y_min, y_max, h))
|
|
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
|
|
|
|
# Put the result into a color plot
|
|
Z = Z.reshape(xx.shape)
|
|
plt.figure()
|
|
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
|
|
|
|
# Plot also the training points
|
|
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
|
|
plt.xlim(xx.min(), xx.max())
|
|
plt.ylim(yy.min(), yy.max())
|
|
plt.title("3-Class classification (k = %i, weights = '%s')"
|
|
% (n_neighbors, weights))
|
|
|
|
plt.show() |