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50 def norm(vec): 51 """ Calculate norm of a vector or list thereof 52 53 Args: 54 vec (Vector or List[Vector]): vector(s) to compute norm of 55 56 Returns: 57 Scalar or List[Scalar]: norms 58 """ 59 if len(vec.shape) == 1: # it's just a single column vector 60 return np.sqrt(vec.dot(vec)) 61 else: # treat as list of vectors 62 return np.sqrt((vec*vec).sum(axis=1))

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69 def norm2(arr): 70 """Returns 1/2 the L2 norm squared.""" 71 return sdot(arr, arr) / 2

81 def norm_1(array): 82 return np.linalg.norm(array, ord=1)

45 def l2_norm(a): 46 ''' Scalar value of word frequency array (vector)''' 47 return math.sqrt(dot(a, a))

186 def norm(self): 187 """ 188 Return the norm of the vector. 189 190 :rtype: :class:`float` 191 """ 192 return sqrt(dot(self, self))

5 def norm(vec, axis=None): 6 return np.sqrt(np.sum(vec**2, axis=axis))

71 def norm(v): 72 """Returns an L2 norm of the vector.""" 73 return math.sqrt(numpy.sum((numpy.array(v)**2).flat))

1138 def norm(a): 1139 1140 return sqrt(scalar_product(a, a))

63 def norm(v): 64 squares = [float(x) * float(x) for x in v] 65 total = sum(squares) 66 sqrt = math.sqrt(total) 67 return sqrt

40 @staticmethod 41 def norm(X): 42 """ 43 Find the frobenius norm of a sparse matrix X 44 """ 45 elements = X.data 46 return numpy.sqrt((elements**2).sum())