12345678910111213141516171819202122232425262728293031323334353637 |
- # import
- # ------------------------------------------------------------------------------------------
- import miam
- import copy
- import numpy as np
- # ------------------------------------------------------------------------------------------
- # MIAM project 2020
- # ------------------------------------------------------------------------------------------
- # author: remi.cozot@univ-littoral.fr
- # ------------------------------------------------------------------------------------------
- class Normalize(object):
- """description of class"""
- def __init__(self,f):
- self.normFunction = f
- def eval(self, u): return self.normFunction(u) # single vector
- def evals(self, u): # array of vector
- res = np.zeros(u.shape)
- for i in range(u.shape[0]): res[i] = self.normFunction(u[i])
- return res
- def cosineNorm(u):
- u1 = copy.deepcopy(u)
- unorm = np.sqrt(np.dot(u1,u1))
- u1 =u1/unorm if unorm != 0.0 else u1
- return u1
- def noNorm(u): return u
- def sortNorm(u):
- u1 = copy.deepcopy(u)
- return np.asarray(sorted(u1.tolist(), key = lambda u : np.sqrt(np.dot(u,u))))
|