57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
# Sebastian Raschka 2014-2020
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# mlxtend Machine Learning Library Extensions
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# Author: Sebastian Raschka <sebastianraschka.com>
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#
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# License: BSD 3 clause
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import numpy as np
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def one_hot(y, num_labels='auto', dtype='float'):
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"""One-hot encoding of class labels
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Parameters
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----------
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y : array-like, shape = [n_classlabels]
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Python list or numpy array consisting of class labels.
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num_labels : int or 'auto'
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Number of unique labels in the class label array. Infers the number
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of unique labels from the input array if set to 'auto'.
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dtype : str
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NumPy array type (float, float32, float64) of the output array.
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Returns
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----------
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ary : numpy.ndarray, shape = [n_classlabels]
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One-hot encoded array, where each sample is represented as
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a row vector in the returned array.
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Examples
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----------
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For usage examples, please see
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http://rasbt.github.io/mlxtend/user_guide/preprocessing/one_hot/
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"""
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if not (num_labels == 'auto' or isinstance(num_labels, int)):
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raise AttributeError('num_labels must be an integer or "auto"')
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if isinstance(y, list):
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yt = np.asarray(y)
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else:
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yt = y
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if not len(yt.shape) == 1:
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raise AttributeError('y array must be 1-dimensional')
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if num_labels == 'auto':
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# uniq = np.unique(yt).shape[0]
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uniq = np.max(yt + 1)
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else:
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uniq = num_labels
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if uniq == 1:
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ary = np.array([[0.]], dtype=dtype)
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else:
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ary = np.zeros((len(y), uniq))
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for i, val in enumerate(y):
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ary[i, val] = 1
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return ary.astype(dtype)
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