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dataset_utils.py
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dataset_utils.py
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import numpy as np
import numpy as np
from sklearn.datasets import make_regression, make_classification
from scipy.io import savemat, loadmat
def load_dataset(name):
if name == "very_sparse":
# l2- regularized sparse least squares
data = loadmat("datasets/very_sparse.mat")
A, b = data['A'].toarray(), data['b']
b = b.ravel()
if name == "binary_sparse":
# l2- regularized sparse least squares
data = loadmat("datasets/very_sparse.mat")
A, b = data['A'].toarray(), data['b']
b = b.ravel()
n_samples = A.shape[0]
block = np.random.choice(np.arange(n_samples), size=n_samples/2, replace=False)
non_block = np.setdiff1d(np.arange(n_samples), block)
b[block] = 1
b[non_block] = -1
if name == "exp1":
# l2- regularized sparse least squares
data = loadmat("datasets/exp1.mat")
A, b = data['X'], data['y']
b = b.ravel()
elif name == "exp2":
# l2- regularized sparse logistic regression
data = loadmat("datasets/exp2.mat")
A, b = data['X'], data['y']
b = b.ravel()
elif name == "exp3":
# Over-determined dense least squares
data = loadmat("datasets/exp3.mat")
A, b = data['X'], data['y']
b = b.ravel()
elif name == "exp4":
# L1 - regularized underdetermined sparse least squares
data = loadmat("datasets/exp4.mat")
A, b = data['X'], data['y']
b = b.ravel()
return {"A":A,"b": b}
def to_categorical(y, nb_classes=None):
'''Convert class vector (integers from 0 to nb_classes)
to binary class matrix, for use with categorical_crossentropy.
'''
if not nb_classes:
nb_classes = np.max(y)+1
Y = np.zeros((len(y), nb_classes))
for i in range(len(y)):
Y[i, y[i]] = 1.
return Y