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26 def 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
29 def one_hot_encoder(a): 30 length = len(a) 31 b = np.zeros( (length, 10) ) 32 b[np.arange(length), a] = 1 33 return b
26 def 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
29 def to_onehot(seq): 30 x = np.zeros((seq.shape[0], 4), dtype=np.float32) 31 alphabet = ["A", "C", "G", "T"] 32 for i in range(len(alphabet)): 33 sel = np.where(seq == alphabet[i]) 34 x[sel[0], i] = 1 35 return x
15 def 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
4 def 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
21 def 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
22 def 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)