10 examples of 'onehotencoder' in Python

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29def one_hot_encoder(a):
30 length = len(a)
31 b = np.zeros( (length, 10) )
32 b[np.arange(length), a] = 1
33 return b
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15def encode_one_hot(s):
16 all = []
17 for c in s:
18 if c not in characters:
19 continue
20 x = np.zeros((INPUT_VOCAB_SIZE))
21 index = char_indices[c]
22 x[index] = 1
23 all.append(x)
24 return all
26def one_hot_encode(X, K):
27 # input is N x D
28 # output is N x D x K
29 N, D = X.shape
30 Y = np.zeros((N, D, K))
31 for n, d in zip(*X.nonzero()):
32 # 0.5...5 --> 1..10 --> 0..9
33 k = int(X[n,d]*2 - 1)
34 Y[n,d,k] = 1
35 return Y
26def onehot(self,arr):
27 n, w, h = arr.shape
28 arr = arr.reshape(n, -1)
29 arr = self.onehotencoder.fit_transform(arr)
30 arr = arr.reshape(n, w, h, self.k)
31 arr = arr.transpose(0, 3, 1, 2)
32 return arr
259def encode(self, label_str, on_value=1, off_value=0): # pylint: disable=arguments-differ
260 e = np.full(self.vocab_size, off_value, dtype=np.int32)
261 e[self._class_labels.index(label_str)] = on_value
262 return e.tolist()
22def decode_one_hot(x):
23 s = []
24 for onehot in x:
25 one_index = np.argmax(onehot)
26 s.append(indices_char[one_index])
27 return ''.join(s)
4def one_hot_encode_y(y, n_classes=135):
5 y = np.asarray(y)
6 if n_classes == 1:
7 return reshape_add_axis(y, len(y.shape)-1)
8 else:
9 from keras.utils import to_categorical
10 shape = y.shape
11 y = to_categorical(y, num_classes=n_classes).astype(np.uint8)
12 y = y.reshape(shape + (n_classes,))
13 return y
55def one_hot_class(inp, n_class):
56 """The function to make the input to n-class one-hot vectors.
57 Args:
58 inp: the input numpy array.
59 n_class: the number of classes.
60 Return:
61 The reorganized n-class one-shot array.
62 """
63 n_sample = inp.shape[0]
64 out = np.zeros((n_sample, n_class))
65 for idx in range(n_sample):
66 out[idx, inp[idx]] = 1
67 return out
21def one_hot(*,
22 index: Union[None, int, Sequence[int]] = None,
23 shape: Union[int, Sequence[int]],
24 value: Any = 1,
25 dtype: Type[np.number]) -> np.ndarray:
26 """Returns a numpy array with all 0s and a single non-zero entry(default 1).
27
28 Args:
29 index: The index that should store the `value` argument instead of 0.
30 If not specified, defaults to the start of the array.
31 shape: The shape of the array.
32 value: The hot value to place at `index` in the result.
33 dtype: The dtype of the array.
34
35 Returns:
36 The created numpy array.
37 """
38 if index is None:
39 index = 0 if isinstance(shape, int) else (0,) * len(shape)
40 result = np.zeros(shape=shape, dtype=dtype)
41 result[index] = value
42 return result
2317def one_hot_encoding(labels, num_classes=None):
2318 """One-hot encodes the multiclass labels.
2319
2320 Example usage:
2321 labels = tf.constant([1, 4], dtype=tf.int32)
2322 one_hot = OneHotEncoding(labels, num_classes=5)
2323 one_hot.eval() # evaluates to [0, 1, 0, 0, 1]
2324
2325 Args:
2326 labels: A tensor of shape [None] corresponding to the labels.
2327 num_classes: Number of classes in the dataset.
2328 Returns:
2329 onehot_labels: a tensor of shape [num_classes] corresponding to the one hot
2330 encoding of the labels.
2331 Raises:
2332 ValueError: if num_classes is not specified.
2333 """
2334 with tf.name_scope('OneHotEncoding', values=[labels]):
2335 if num_classes is None:
2336 raise ValueError('num_classes must be specified')
2337
2338 labels = tf.one_hot(labels, num_classes, 1, 0)
2339 return tf.reduce_max(labels, 0)

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