%matplotlib inline

# set seaborn plot defaults.
# This can be safely commented out
import seaborn; seaborn.set()

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

#Uncomment the %load command to load the contents of the file
# %load fig_code/sgd_separator.py

from fig_code import plot_linear_regression
plot_linear_regression()

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

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

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

from sklearn.datasets import load_iris
iris = load_iris()

iris.keys()

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

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

print(iris.target)

print(iris.target_names)

import numpy as np
import matplotlib.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, cmap=plt.cm.get_cmap('RdYlBu', 3))
plt.colorbar(ticks=[0, 1, 2], format=formatter)
plt.clim(-0.5, 2.5)
plt.xlabel(iris.feature_names[x_index])
plt.ylabel(iris.feature_names[y_index]);

from sklearn import datasets

# Type datasets.fetch_<TAB> or datasets.load_<TAB> in IPython to see all possibilities

# datasets.fetch_

# datasets.load_