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import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model, datasets
 
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
Y = iris.target
 
h = .02  # step size in the mesh
 
logreg = linear_model.LogisticRegression(C=1e5)
 
# we create an instance of Neighbours Classifier and fit the data.
logreg.fit(X, Y)
 
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])
 
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure(1, figsize=(4, 3))
plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
 
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
 
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
 
plt.show()
 
 

Main.java:1: error: '.' expected
import numpy as np
            ^
Main.java:1: error: ';' expected
import numpy as np
               ^
Main.java:2: error: ';' expected
import matplotlib.pyplot as plt
                        ^
Main.java:3: error: '.' expected
from sklearn import linear_model, datasets
                                ^
Main.java:3: error: ';' expected
from sklearn import linear_model, datasets
                                 ^
Main.java:5: error: illegal character: '#'
# import some data to play with
^
Main.java:5: error: class, interface, or enum expected
# import some data to play with
         ^
Main.java:7: error: illegal character: '#'
X = iris.data[:, :2]  # we only take the first two features.
                      ^
Main.java:10: error: illegal character: '#'
h = .02  # step size in the mesh
         ^
Main.java:14: error: illegal character: '#'
# we create an instance of Neighbours Classifier and fit the data.
^
Main.java:17: error: illegal character: '#'
# Plot the decision boundary. For that, we will assign a color to each
^
Main.java:18: error: illegal character: '#'
# point in the mesh [x_min, x_max]x[y_min, y_max].
^
Main.java:24: error: illegal character: '#'
# Put the result into a color plot
^
Main.java:29: error: illegal character: '#'
# Plot also the training points
^
Main.java:31: error: unclosed character literal
plt.xlabel('Sepal length')
           ^
Main.java:31: error: unclosed character literal
plt.xlabel('Sepal length')
                        ^
Main.java:32: error: unclosed character literal
plt.ylabel('Sepal width')
           ^
Main.java:32: error: unclosed character literal
plt.ylabel('Sepal width')
                       ^
18 errors

Standard output is empty