# Start pylab inline mode, so figures will appear in the notebook
%matplotlib inline

# Import the example plot from the figures directory
from figures import plot_sgd_separator
plot_sgd_separator()

from figures import plot_linear_regression
plot_linear_regression()

from IPython.core.display import Image, display
display(Image(filename='figures/iris_setosa.jpg'))
print("Iris Setosa\n")

display(Image(filename='figures/iris_versicolor.jpg'))
print("Iris Versicolor\n")

display(Image(filename='figures/iris_virginica.jpg'))
print("Iris Virginica")

from sklearn.datasets import load_iris
iris = load_iris()

n_samples, n_features = iris.data.shape
print(n_samples)
print(n_features)
print(iris.data[0])

print(iris.data.shape)
print(iris.target.shape)

print(iris.target)

print(iris.target_names)

from matplotlib import pyplot as plt

x_index = 0
y_index = 1

# this formatter will label the colorbar with the correct target names
formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])

plt.scatter(iris.data[:, x_index], iris.data[:, y_index], c=iris.target)
plt.colorbar(ticks=[0, 1, 2], format=formatter)
plt.xlabel(iris.feature_names[x_index])
plt.ylabel(iris.feature_names[y_index])