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

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

def plot_iris_projection(x_index, y_index):
    # 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])
    
plot_iris_projection(2, 3)

from sklearn import datasets

from sklearn.datasets import get_data_home
get_data_home()

!ls $HOME/scikit_learn_data/

from sklearn.datasets import load_digits
digits = load_digits()

digits.keys()

n_samples, n_features = digits.data.shape
print (n_samples, n_features)

print digits.data[0]
print digits.target

print digits.data.shape
print digits.images.shape

print np.all(digits.images.reshape((1797, 64)) == digits.data)

print digits.data.__array_interface__['data']
print digits.images.__array_interface__['data']

# set up the figure
fig = plt.figure(figsize=(6, 6))  # figure size in inches
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)

# plot the digits: each image is 8x8 pixels
for i in range(64):
    ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])
    ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')
    
    # label the image with the target value
    ax.text(0, 7, str(digits.target[i]))

from sklearn.datasets import make_s_curve
data, colors = make_s_curve(n_samples=1000)
print(data.shape)
print(colors.shape)

from mpl_toolkits.mplot3d import Axes3D
ax = plt.axes(projection='3d')
ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=colors)
ax.view_init(10, -60)

from sklearn.datasets import fetch_olivetti_faces

# fetch the faces data


# Use a script like above to plot the faces image data.
# hint: plt.cm.bone is a good colormap for this data


# Uncomment the following to load the solution to this exercise
# %load solutions/02_faces.py