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type1.py
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import numpy as np
def sim_def_lin(model, test_instances, test_targets, training_instances,
training_targets, batch_size, anynominal, colmap):
tot = np.concatenate((test_instances,training_instances),axis=0)
tot_maximums = np.max(tot,axis=0)
tot_minmums = np.min(tot,axis=0)
res = list()
nominallist = list()
if anynominal:
for key,value in colmap.items():
#if value["class"]:
# continue
if value["type"] is "nominal":
nominallist.append(True)
else:
nominallist.append(False)
for test_instance,test_target in zip(test_instances,test_targets):
max_sim=0
most_similar_case_target = None
for training_instance,training_target in zip(training_instances,training_targets):
sim = global_sim_def(test_instance,training_instance,tot_maximums,tot_minmums,anynominal,nominallist)
if sim > max_sim:
max_sim = sim
most_similar_case_target = training_target
if (most_similar_case_target is not None) and (np.rint(test_target) == np.rint(most_similar_case_target)).all():
res.append(1)
else:
res.append(0)
return res
def global_sim_def(v1,v2,maximums,minimums,anynominal, nominallist):
res = 0
if not anynominal:
for i in range(0,v1.shape[0]):
res = res + local_sim_n_def(v1[i],v2[i],maximums[i],minimums[i])
else:
for i in range(0, v1.shape[0]):
if nominallist[i] is True:
res = res + local_sim_s_def(v1[i], v2[i])
else:
res = res + local_sim_n_def(v1[i], v2[i], maximums[i], minimums[i])
ret = res/len(v1)
return ret
def local_sim_n_def(v1,v2,d_max,d_min):
return 1-(abs(v1-v2)/(d_max-d_min))
def local_sim_s_def(v1,v2):
if v1 == v2:
return 1
else:
return 0
def getValue(arg0,param,max_val,min_val):
val1 = min_val
val2 = abs(arg0)+min_val
if arg0 > 0:
return sim_nonlin(val1,val2,param,max_val,min_val)
else:
return sim_nonlin(val2,val1,param,max_val,min_val)
def sim_nonlin(q,c,paramL,max_val,min_val):
d = q-c
maxrange = max_val-min_val # max-min
if d < 0:
return f(d,paramL,maxrange)
else:
return f(d,paramL,-maxrange)
def f(value,exponent,diff):
return myfilter(np.power(value/diff + 1.0, exponent))
def mySimFct(c1, c2, param, diff):
return
def myrounder(num):
return round(num*100,0)/100.0
def myfilter(num):
if num < 0.0 or num > 1:
return -1
else:
return num
def global_sim_nonlin(v1,v2,maximums,minimums,anynominal, nominallist, params):
res = 0
if not anynominal:
for i in range(0,v1.shape[0]):
res = res + sim_nonlin(v1[i],v2[i],params[i],maximums[i],minimums[i])
else:
for i in range(0, v1.shape[0]):
if nominallist[i] is True:
res = res + local_sim_s_def(v1[i], v2[i])
else:
res = res + sim_nonlin(v1[i],v2[i],params[i],maximums[i],minimums[i])
ret = res/len(v1)
return ret
def sim_def_nonlin(model, test_instances, test_targets, training_instances,
training_targets, batch_size, anynominal, colmap):
tot = np.concatenate((test_instances,training_instances),axis=0)
tot_maximums = np.max(tot,axis=0)
tot_minmums = np.min(tot,axis=0)
params = np.zeros((tot.shape[1],1))
for i in range(0,tot.shape[1]):
q1 = np.percentile(tot[:,i],25)
q3 = np.percentile(tot[:,i],75)
params[i] = abs(q1-q3)
res = list()
nominallist = list()
if anynominal:
for key,value in colmap.items():
#if value["class"]:
# continue
if value["type"] is "nominal":
nominallist.append(True)
else:
nominallist.append(False)
for test_instance,test_target in zip(test_instances,test_targets):
max_sim=0
most_similar_case_target = None
for training_instance,training_target in zip(training_instances,training_targets):
sim = global_sim_nonlin(test_instance,training_instance,tot_maximums,tot_minmums,anynominal,nominallist,params)
if sim > max_sim:
max_sim = sim
most_similar_case_target = training_target
if (most_similar_case_target is not None) and (np.rint(test_target) == np.rint(most_similar_case_target)).all():
res.append(1)
else:
res.append(0)
return res