Convert array of indices to 1-hot encoded numpy array
Your array a defines the columns of the nonzero elements in the output array. You need to also define the rows and then use fancy indexing:
>>> a = np.array([1, 0, 3])>>> b = np.zeros((a.size, a.max()+1))>>> b[np.arange(a.size),a] = 1>>> barray([[ 0., 1., 0., 0.], [ 1., 0., 0., 0.], [ 0., 0., 0., 1.]])
>>> values = [1, 0, 3]>>> n_values = np.max(values) + 1>>> np.eye(n_values)[values]array([[ 0., 1., 0., 0.], [ 1., 0., 0., 0.], [ 0., 0., 0., 1.]])
In case you are using keras, there is a built in utility for that:
from keras.utils.np_utils import to_categorical categorical_labels = to_categorical(int_labels, num_classes=3)And it does pretty much the same as @YXD's answer (see source-code).