def split_trees(trees, tree_labels, n_folds=10, shuffle=True):
classes_, y = np.unique(tree_labels, return_inverse=True)
tree_labels = y
if shuffle:
indices = np.arange(trees.shape[0])
random.shuffle(indices)
trees = trees[indices]
tree_labels = tree_labels[indices]
# classes_ = np.arange(len(classes_))
cv = StratifiedKFold(tree_labels, n_folds=n_folds, shuffle=shuffle)
train_indices, test_indices = next(cv.__iter__())
train_trees, train_lables = trees[train_indices], tree_labels[
train_indices]
test_trees, test_lables = trees[test_indices], tree_labels[test_indices]
return train_trees, train_lables, test_trees, test_lables, classes_
angle = np.arctan2(u[1], u[0])
angle = 180 * angle / np.pi # convert to degrees
v *= 9
ell = mpl.patches.Ellipse(gmm.means_[n, :2], v[0], v[1], 180 + angle, color=color)
ell.set_clip_box(ax.bbox)
ell.set_alpha(0.5)
ax.add_artist(ell)
iris = datasets.load_iris()
# Break up the dataset into non-overlapping training (75%) and testing
# (25%) sets.
skf = StratifiedKFold(iris.target, k=4)
# Only take the first fold.
train_index, test_index = skf.__iter__().next()
X_train = iris.data[train_index]
y_train = iris.target[train_index]
X_test = iris.data[test_index]
y_test = iris.target[test_index]
n_classes = len(np.unique(y_train))
# Try GMMs using different types of covariances.
classifiers = dict((x, GMM(n_components=n_classes, covariance_type=x)) for x in ["spherical", "diag", "tied", "full"])
n_classifiers = len(classifiers)
pl.figure(figsize=(3 * n_classifiers / 2, 6))
angle = np.arctan(u[1] / u[0])
angle = 180 * angle / np.pi # convert to degrees
v *= 9
ell = mpl.patches.Ellipse(gmm.means[n, :2], v[0], v[1], 180 + angle,
color=color)
ell.set_clip_box(ax.bbox)
ell.set_alpha(0.5)
ax.add_artist(ell)
iris = datasets.load_iris()
# Break up the dataset into non-overlapping training (75%) and testing
# (25%) sets.
skf = StratifiedKFold(iris.target, k=4)
# Only take the first fold.
train_index, test_index = skf.__iter__().next()
X_train = iris.data[train_index]
y_train = iris.target[train_index]
X_test = iris.data[test_index]
y_test = iris.target[test_index]
n_classes = len(np.unique(y_train))
# Try GMMs using different types of covariances.
classifiers = dict((x, GMM(n_components=n_classes, cvtype=x))
for x in ['spherical', 'diag', 'tied', 'full'])
n_classifiers = len(classifiers)
def split_dataframe(df):
kf = StratifiedKFold(df["OpenStatus"].values, 5)
train, test = kf.__iter__().next()
return df.take(train), df.take(test)
