Data points without K Nearest Neighbour¶
In [ ]:
import matplotlib
#matplotlib.use('GTKAgg')
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets
# import some data to play with
iris = datasets.load_iris()
# take the first two features
# choosing first 2 columns sepal length and sepal width
X = iris.data[:, :2]
y = iris.target
h = .02 # step size in the mesh
# Calculate min, max and limits
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))
# Put the result into a color plot
plt.figure()
plt.scatter(X[:, 0], X[:, 1])
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title("Data points")
plt.show()
Sepal length and sepal width analysis¶
In [ ]:
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets
n_neighbors = 6
# import some data to play with
iris = datasets.load_iris()
# prepare data
# choosing first 2 columns sepal length and sepal width
X = iris.data[:, :2]
y = iris.target
h = .02
# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA','#00AAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00','#00AAFF'])
# we create an instance of Neighbours Classifier and fit the data.
clf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
clf.fit(X, y)
# calculate min, max and limits
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))
# predict class using data and kNN classifier
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
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)" % (n_neighbors))
plt.show()
Petal length and petal width analysis¶
In [ ]:
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets
n_neighbors = 6
# import some data to play with
iris = datasets.load_iris()
# prepare data
# choosing 3rd and 4th columns petal length and petal width
X = iris.data[:, [2,3]]
y = iris.target
h = .02
# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA','#00AAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00','#00AAFF'])
# we create an instance of Neighbours Classifier and fit the data.
clf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
clf.fit(X, y)
# calculate min, max and limits
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))
# predict class using data and kNN classifier
print(np.c_[xx.ravel(), yy.ravel()])
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
print(Z)
# Put the result into a color plot
Z = Z.reshape(xx.shape)
print(Z)
plt.figure()
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
plt.figure()
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
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)" % (n_neighbors))
plt.show()
Predicting the output¶
In [ ]:
import numpy as np
from sklearn import neighbors, datasets
from sklearn import preprocessing
n_neighbors = 6
# import some data to play with
iris = datasets.load_iris()
# prepare data
# choosing first 2 columns sepal length and sepal width
X = iris.data[:, :2]
y = iris.target
h = .02
# we create an instance of Neighbours Classifier and fit the data.
clf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
clf.fit(X, y)
# make prediction
#sl = raw_input('Enter sepal length (cm): ')
#sw = raw_input('Enter sepal width (cm): ')
sl = 6.4
sw = 2.8
dataClass = clf.predict([[sl,sw]])
print('Prediction: '),
if dataClass == 0:
print('Iris Setosa')
elif dataClass == 1:
print('Iris Versicolour')
else:
print('Iris Virginica')
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