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#
# Copyright (c) 2013-present, Anoop Kunchukuttan
# All rights reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
from indicnlp import loader
from indicnlp import langinfo
from indicnlp.script.indic_scripts import *
import numpy as np
import gzip
import pandas as pd
import sys
def equal(v1,v2):
return 0.0 if np.sum( xor_vectors(v1, v2)) > 0 else 1.0
def dice(v1,v2):
dotprod=2*float(np.dot( v1, v2.T ))
return dotprod/float(len(v1)+len(v2))
def jaccard(v1,v2):
dotprod=float(np.dot( v1, v2.T ))
return dotprod/float(len(v1)+len(v2)-dotprod)
def cosine(v1,v2):
dotprod=float(np.dot( v1, v2.T ))
norm1=float(np.dot( v1, v1.T ))
norm2=float(np.dot( v2, v2.T ))
return ((dotprod)/(np.sqrt(norm1*norm2)+0.00001))
def dotprod(v1,v2):
return float(np.dot( v1, v2.T ))
def sim1(v1,v2,base=5.0):
return np.power(base,dotprod(v1,v2))
def softmax(v1,v2):
return sim1(v1,v2,np.e)
def create_similarity_matrix(sim_func,slang,tlang,normalize=True):
dim=langinfo.COORDINATED_RANGE_END_INCLUSIVE-langinfo.COORDINATED_RANGE_START_INCLUSIVE+1
sim_mat=np.zeros((dim,dim))
for offset1 in range(langinfo.COORDINATED_RANGE_START_INCLUSIVE, langinfo.COORDINATED_RANGE_END_INCLUSIVE+1):
v1=get_phonetic_feature_vector(offset_to_char(offset1,slang),slang)
for offset2 in range(langinfo.COORDINATED_RANGE_START_INCLUSIVE, langinfo.COORDINATED_RANGE_END_INCLUSIVE+1):
v2=get_phonetic_feature_vector(offset_to_char(offset2,tlang),tlang)
sim_mat[offset1,offset2]=sim_func(v1,v2)
if normalize:
sums=np.sum(sim_mat, axis=1)
sim_mat=(sim_mat.transpose()/sums).transpose()
return sim_mat
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