import logging
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from mla.tsne import TSNE
logging.basicConfig(level=logging.DEBUG)
X, y = make_classification(n_samples=500, n_features=10, n_informative=5, n_redundant=0, random_state=1111,
n_classes=2, class_sep=2.5, )
p = TSNE(2, max_iter=500)
X = p.fit_transform(X)
colors = ['red', 'green']
for t in range(2):
t_mask = (y == t).astype(bool)
plt.scatter(X[t_mask, 0], X[t_mask, 1], color=colors[t])
plt.show()
try:
from sklearn.model_selection import train_test_split
except ImportError:
from sklearn.cross_validation import train_test_split
from mla.linear_models import LogisticRegression
from mla.metrics import accuracy
from mla.pca import PCA
# logging.basicConfig(level=logging.DEBUG)
# Generate a random binary classification problem.
X, y = make_classification(n_samples=1000, n_features=100, n_informative=75,
random_state=1111, n_classes=2, class_sep=2.5, )
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,
random_state=1111)
for s in ['svd', 'eigen']:
p = PCA(15, solver=s)
import logging
import matplotlib.pyplot as pltfrom sklearn.datasets import make_classification
from mla.tsne import TSNE
logging.basicConfig(level=logging.DEBUG)
X, y = make_classification(n_samples=500, n_features=10, n_informative=5, n_redundant=0, random_state=1111, n_classes=2, class_sep=2.5, )
p = TSNE(2, max_iter=500)X = p.fit_transform(X)
colors = ['red', 'green']for t in range(2): t_mask = (y == t).astype(bool) plt.scatter(X[t_mask, 0], X[t_mask, 1], color=colors[t])
plt.show()try: from sklearn.model_selection import train_test_splitexcept ImportError: from sklearn.cross_validation import train_test_splitfrom sklearn.datasets import make_classification
from mla.linear_models import LogisticRegressionfrom mla.metrics import accuracyfrom mla.pca import PCA
# logging.basicConfig(level=logging.DEBUG)
# Generate a random binary classification problem.X, y = make_classification(n_samples=1000, n_features=100, n_informative=75, random_state=1111, n_classes=2, class_sep=2.5, )
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1111)
for s in ['svd', 'eigen']: p = PCA(15, solver=s)