machinelearning_notebook/tips/datasets.py

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# ## Datasets

# ## Moons
#

# +
% matplotlib inline
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt

# generate sample data
np.random.seed(0)
X, y = datasets.make_moons(200, noise=0.20)

# plot data
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)
plt.show()
# -

# ## XOR

# +
import numpy as np
import matplotlib.pyplot as plt

from sklearn.gaussian_process import GaussianProcessClassifier

rng = np.random.RandomState(0)
X = rng.randn(200, 2)
Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)

# plot data
plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Spectral)
plt.show()
# -

# ## Digital 

# +
import matplotlib.pyplot as plt 
from sklearn.datasets import load_digits

# load data
digits = load_digits()

# copied from notebook 02_sklearn_data.ipynb
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)
    
    # label the image with the target value
    ax.text(0, 7, str(digits.target[i]))