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%matplotlib inline
import ipywidgets as widgets
import numpy as np
from ipywidgets import interact, interactive, fixed, interact_manual
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import SGDClassifier
This data sets consists of 3 different types of irises' (Setosa, Versicolour, and Virginica) petal and sepal length, stored in a 150x4 numpy.ndarray
The rows being the samples and the columns being: Sepal Length, Sepal Width, Petal Length and Petal Width.
The below plot uses the first two features.
See here <https://en.wikipedia.org/wiki/Iris_flower_data_set>_ for more
information on this dataset.
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print(__doc__)
# Code source: Gaël Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
from sklearn.decomposition import PCA
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
y = iris.target
x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
plt.figure(2, figsize=(8, 6))
plt.clf()
# Plot the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,
edgecolor='k')
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
# To getter a better understanding of interaction of the dimensions
# plot the first three PCA dimensions
fig = plt.figure(1, figsize=(8, 6))
ax = Axes3D(fig, elev=-150, azim=110)
X_reduced = PCA(n_components=3).fit_transform(iris.data)
ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=y,
cmap=plt.cm.Set1, edgecolor='k', s=40)
ax.set_title("First three PCA directions")
ax.set_xlabel("1st eigenvector")
ax.w_xaxis.set_ticklabels([])
ax.set_ylabel("2nd eigenvector")
ax.w_yaxis.set_ticklabels([])
ax.set_zlabel("3rd eigenvector")
ax.w_zaxis.set_ticklabels([])
plt.show()
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# Plot nth feature
@interact(n=(1,4))
def plot_nth(n=1):
X = iris.data[:, n-1:n]
Y = np.expand_dims(iris.target, axis=1)
plt.scatter(X, Y)
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# Gaussian classifier and training
classifier = GaussianNB()
classifier.fit(iris.data, iris.target)
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classifier.predict(np.array([[1,9,0,1]]))
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# Transform training outputs
fun = lambda x: 1 if x == 1 else 0
Y = np.array(list(map(fun, iris.target)))
X = iris.data[:, :1]
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# Logistic classifier and training
log_classifier = None
@interact(loss=['log', 'hinge', 'perceptron'], penalty=['none', 'l1', 'l2'])
def log_classify(loss='log', penalty=None):
global log_classifier
log_classifier = SGDClassifier(loss=loss, penalty=penalty, max_iter=1000)
log_classifier.fit(X, Y)
print(log_classifier.coef_)
print(log_classifier.intercept_)
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sample_x = np.expand_dims(np.linspace(0, 8), axis=1)
sample_y = log_classifier.intercept_ + (sample_x * log_classifier.coef_)
plt.scatter(X, Y)
plt.plot(sample_x, sample_y, color='r')
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log_classifier.coef_[:,]
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