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

# 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()

import numpy as np

# Generating a random array
X = np.random.random((3, 5))  # a 3 x 5 array

print X

# Accessing elements

# get a single element
print X[0, 0]

# get a row
print X[1]

# get a column
print X[:, 1]

# Transposing an array
print X.T

# Turning a row vector into a column vector
y = np.linspace(0, 12, 5)
print y

# make into a column vector
print y[:, np.newaxis]

# Create a random array with a lot of zeros
X = np.random.random((10, 5))
print X

X[X < 0.7] = 0
print X

from scipy import sparse

# turn X into a csr (Compressed-Sparse-Row) matrix
X_csr = sparse.csr_matrix(X)
print X_csr

# convert the sparse matrix to a dense array
print X_csr.toarray()

# Sparse matrices support linear algebra:
y = np.random.random(X_csr.shape[1])
z1 = X_csr.dot(y)
z2 = X.dot(y)
np.allclose(z1, z2)

# Create an empty LIL matrix and add some items
X_lil = sparse.lil_matrix((5, 5))

for i, j in np.random.randint(0, 5, (15, 2)):
    X_lil[i, j] = i + j

print X_lil
print X_lil.toarray()

X_csr = X_lil.tocsr()
print X_csr

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

# plotting a line

x = np.linspace(0, 10, 100)
plt.plot(x, np.sin(x));

# scatter-plot points

x = np.random.normal(size=500)
y = np.random.normal(size=500)
plt.scatter(x, y);

# showing images
x = np.linspace(1, 12, 100)
y = x[:, np.newaxis]

im = y * np.sin(x) * np.cos(y)
print(im.shape)

# imshow - note that origin is at the top-left!
plt.imshow(im);

# Contour plot - note that origin here is at the bottom-left!
plt.contour(im);