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Updated util_knn.py to new version of scikit

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cif2cif 2021-02-27 20:11:17 +01:00
parent 5144b7f228
commit 2f7cbe9e45
1 changed files with 13 additions and 9 deletions
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ml1/util_knn.py
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@ -2,6 +2,7 @@ import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets
import seaborn as sns
from sklearn.neighbors import KNeighborsClassifier
# Taken from http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html
@ -20,8 +21,8 @@ def plot_classification_iris():
n_neighbors = 15
# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])
cmap_bold = ['darkorange', 'c', 'darkblue']
for weights in ['uniform', 'distance']:
# we create an instance of Neighbours Classifier and fit the data.
@ -29,7 +30,7 @@ def plot_classification_iris():
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].
# point in the mesh [x_min, x_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),
@ -38,14 +39,17 @@ def plot_classification_iris():
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
plt.figure(figsize=(8, 6))
plt.contourf(xx, yy, Z, cmap=cmap_light)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=iris.target_names[y],
palette=cmap_bold, alpha=1.0, edgecolor="black")
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.xlabel(iris.feature_names[0])
plt.ylabel(iris.feature_names[1])
plt.show()