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new_by_persistence.md

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import os
import gudhi
import json
import numpy as np
from functools import partial
import classifier
import sklearn.metrics as metrics
from importlib import reload
from sklearn.cluster import DBSCAN
from sklearn.model_selection import train_test_split
import region
import shapely as sh
from importlib import reload
def get_persistence_dict(file_name):
    with open(file_name,'r') as f:
        data_dict=json.load(f)
    return data_dict
reload(classifier)
reload(region)
<module 'region' from 'D:\\PROJECT\\seminconductor\\code\\data2\\region.py'>
basic_work_path=os.path.join(os.getcwd(),'result','complex')
complex_list=os.listdir(basic_work_path)
file_list=[]
for complex_ in complex_list:
    work_path=os.path.join(basic_work_path,complex_)
    tmp=os.listdir(work_path)
    for file in tmp:
        file_list.append(os.path.join(work_path,file))
data_dict=get_persistence_dict(file_list[47])
basic_work_path=os.path.join(os.getcwd(),'result','complex')
complex_list=os.listdir(basic_work_path)
file_list=[]
for complex_ in complex_list:
    work_path=os.path.join(basic_work_path,complex_)
    tmp=os.listdir(work_path)
    for file in tmp:
        file_list.append(os.path.join(work_path,file))
"""
basic_save_path=os.path.join(os.getcwd(),'result','result')
for file in file_list:
    _,name=os.path.split(file)
    f=open(os.path.join(basic_save_path,name),'w')
    data_dict=get_persistence_dict(file)
    train_dict={}
    test_dict={}
    train_label_dict={}
    test_label_dict={}
    train_dict['good'],test_dict['good'],train_label_dict['good'],test_label_dict['good']=train_test_split(data_dict['good'],[0 for _ in range(len(data_dict['good']))],test_size=0.25)
    test_dict['bad']=data_dict['bad']
    test_label_dict['bad']=[1 for _ in range(len(data_dict['bad']))]
    
    cl=classifier.Classifier()
    cl._make_region(train_dict['good'])
    
    test_list=[data for key in test_dict.keys() for data in test_dict[key]]
    test_label_list=[0 for _ in range(len(test_dict['good']))]+[1 for _ in range(len(test_dict['bad']))]
    predict_label_list=[]
    for test in test_list:
        predict_label_list.append(cl.predict(test))    
    result=(metrics.classification_report(test_label_list,predict_label_list)
    f.write(result)
    f.close()
"""
file=file_list[5]
    #_,name=os.path.split(file)
    
    #data_dict=get_persistence_dict(file)
    train_dict={}
    test_dict={}
    train_label_dict={}
    test_label_dict={}
    train_dict['good'],test_dict['good'],train_label_dict['good'],test_label_dict['good']=train_test_split(data_dict['good'],[0 for _ in range(len(data_dict['good']))],test_size=0.25)
    test_dict['bad']=data_dict['bad']
    test_label_dict['bad']=[1 for _ in range(len(data_dict['bad']))]
reload(classifier)
reload(region)    
cl=classifier.Classifier()
cl._make_region(train_dict['good'])
    test_list=[data for key in test_dict.keys() for data in test_dict[key]]
    test_label_list=[0 for _ in range(len(test_dict['good']))]+[1 for _ in range(len(test_dict['bad']))]
#predict_label_list=cl.predict(test_dict['good'])
#'''
predict_label_list=[]
for test in test_list:
    predict_label_list.append(cl.predict(test))
#'''
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---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-96-0325d05319f8> in <module>
      3 predict_label_list=[]
      4 for test in test_list:
----> 5     predict_label_list.append(cl.predict(test))
      6 #'''


D:\PROJECT\seminconductor\code\data2\classifier.py in predict(self, data_set)
     71         for key in data_dict_by_betti.keys():
     72             persistence_result.append(self.region_dict[key].predict(data_dict_by_betti[key]))
---> 73         result=reduce(lambda x,y : x*y, persistence_result)
     74         print('result: ', result)
     75 


TypeError: reduce() of empty sequence with no initial value
result=metrics.classification_report(test_label_list,predict_label_list)
print(result)
              precision    recall  f1-score   support

           0       0.00      0.00      0.00        82
           1       0.34      0.96      0.51        45

    accuracy                           0.34       127
   macro avg       0.17      0.48      0.25       127
weighted avg       0.12      0.34      0.18       127
test
[[0, [0.0, inf]]]
for region_key in cl.region_dict.keys():
    for convex_key in cl.region_dict[region_key].region_dict.keys():
        print('region key: ',region_key,'convex_key: ',convex_key,type(cl.region_dict[region_key].region_dict[convex_key].polygon),convex_key,type(cl.region_dict[region_key].region_dict[convex_key].polygon)==sh.geometry.polygon.Polygon)
region key:  4 convex_key:  0 <class 'shapely.geometry.polygon.Polygon'> 0 True
region key:  3 convex_key:  0 <class 'shapely.geometry.polygon.Polygon'> 0 True
region key:  3 convex_key:  1 <class 'shapely.geometry.linestring.LineString'> 1 False
region key:  3 convex_key:  2 <class 'shapely.geometry.linestring.LineString'> 2 False
region key:  2 convex_key:  0 <class 'shapely.geometry.polygon.Polygon'> 0 True
region key:  1 convex_key:  0 <class 'shapely.geometry.polygon.Polygon'> 0 True
region key:  0 convex_key:  0 <class 'shapely.geometry.point.Point'> 0 False
region key:  0 convex_key:  1 <class 'shapely.geometry.point.Point'> 1 False
region key:  0 convex_key:  2 <class 'shapely.geometry.point.Point'> 2 False
region key:  0 convex_key:  3 <class 'shapely.geometry.linestring.LineString'> 3 False
region key:  0 convex_key:  4 <class 'shapely.geometry.linestring.LineString'> 4 False
region key:  0 convex_key:  5 <class 'shapely.geometry.linestring.LineString'> 5 False
region key:  0 convex_key:  6 <class 'shapely.geometry.point.Point'> 6 False
region key:  0 convex_key:  7 <class 'shapely.geometry.point.Point'> 7 False
region key:  0 convex_key:  8 <class 'shapely.geometry.linestring.LineString'> 8 False
region key:  0 convex_key:  9 <class 'shapely.geometry.point.Point'> 9 False
region key:  0 convex_key:  10 <class 'shapely.geometry.linestring.LineString'> 10 False
region key:  0 convex_key:  11 <class 'shapely.geometry.point.Point'> 11 False
region key:  0 convex_key:  12 <class 'shapely.geometry.linestring.LineString'> 12 False
region key:  0 convex_key:  13 <class 'shapely.geometry.point.Point'> 13 False
region key:  0 convex_key:  14 <class 'shapely.geometry.point.Point'> 14 False
region key:  0 convex_key:  15 <class 'shapely.geometry.linestring.LineString'> 15 False
region key:  0 convex_key:  16 <class 'shapely.geometry.linestring.LineString'> 16 False
region key:  0 convex_key:  17 <class 'shapely.geometry.linestring.LineString'> 17 False
region key:  0 convex_key:  18 <class 'shapely.geometry.point.Point'> 18 False
region key:  0 convex_key:  19 <class 'shapely.geometry.point.Point'> 19 False
region key:  0 convex_key:  20 <class 'shapely.geometry.linestring.LineString'> 20 False
region key:  0 convex_key:  21 <class 'shapely.geometry.linestring.LineString'> 21 False
region key:  0 convex_key:  22 <class 'shapely.geometry.point.Point'> 22 False
region key:  0 convex_key:  23 <class 'shapely.geometry.point.Point'> 23 False
region key:  0 convex_key:  24 <class 'shapely.geometry.point.Point'> 24 False
region key:  0 convex_key:  25 <class 'shapely.geometry.point.Point'> 25 False
region key:  0 convex_key:  26 <class 'shapely.geometry.point.Point'> 26 False
region key:  0 convex_key:  27 <class 'shapely.geometry.point.Point'> 27 False
region key:  0 convex_key:  28 <class 'shapely.geometry.point.Point'> 28 False
region key:  0 convex_key:  29 <class 'shapely.geometry.point.Point'> 29 False
region key:  0 convex_key:  30 <class 'shapely.geometry.linestring.LineString'> 30 False
region key:  0 convex_key:  31 <class 'shapely.geometry.linestring.LineString'> 31 False
region key:  0 convex_key:  32 <class 'shapely.geometry.point.Point'> 32 False
region key:  0 convex_key:  33 <class 'shapely.geometry.point.Point'> 33 False
region key:  0 convex_key:  34 <class 'shapely.geometry.point.Point'> 34 False
region key:  0 convex_key:  35 <class 'shapely.geometry.point.Point'> 35 False
region key:  0 convex_key:  36 <class 'shapely.geometry.linestring.LineString'> 36 False
region key:  0 convex_key:  37 <class 'shapely.geometry.linestring.LineString'> 37 False
region key:  0 convex_key:  38 <class 'shapely.geometry.point.Point'> 38 False
region key:  0 convex_key:  39 <class 'shapely.geometry.point.Point'> 39 False
region key:  0 convex_key:  40 <class 'shapely.geometry.linestring.LineString'> 40 False
region key:  0 convex_key:  41 <class 'shapely.geometry.point.Point'> 41 False
region key:  0 convex_key:  42 <class 'shapely.geometry.point.Point'> 42 False
region key:  0 convex_key:  43 <class 'shapely.geometry.point.Point'> 43 False
region key:  0 convex_key:  44 <class 'shapely.geometry.point.Point'> 44 False
region key:  0 convex_key:  45 <class 'shapely.geometry.point.Point'> 45 False
region key:  0 convex_key:  46 <class 'shapely.geometry.point.Point'> 46 False
region key:  0 convex_key:  47 <class 'shapely.geometry.linestring.LineString'> 47 False
region key:  0 convex_key:  48 <class 'shapely.geometry.point.Point'> 48 False
region key:  0 convex_key:  49 <class 'shapely.geometry.point.Point'> 49 False
region key:  0 convex_key:  50 <class 'shapely.geometry.point.Point'> 50 False
region key:  0 convex_key:  51 <class 'shapely.geometry.point.Point'> 51 False
print('predict: ',cl.predict(test_list[0]))
for i, cor in enumerate(test_list[0]):
    betti, cord=cor
    for key in cl.region_dict[betti].region_dict.keys():
        if type(cl.region_dict[betti].region_dict[key].polygon)==sh.geometry.polygon.Polygon:
            x,y=cl.region_dict[betti].region_dict[key].polygon.exterior.coords.xy
            x=np.array(x)
            y=np.array(y)
            x_min=x.min()
            x_max=x.max()
            y_min=y.min()
            y_max=y.max()
            print('Polygon: ',i,betti,key,cord,x_min,x_max,y_min,y_max,cl.region_dict[betti].region_dict[key].predict(cord,0.5))
        if type(cl.region_dict[betti].region_dict[key].polygon)==sh.geometry.linestring.LineString:
            x,y=cl.region_dict[betti].region_dict[key].polygon.coords.xy
            x=np.array(x)
            y=np.array(y)            
            cord_=[]
            for j in range(len(x)):
                cord_.append([x[j],y[j]])
            print('Line: ',i,betti,key,cord,cord_[0],cord_[1],cl.region_dict[betti].region_dict[key].predict(cord,0.5))
        if type(cl.region_dict[betti].region_dict[key].polygon)==sh.geometry.point.Point:
            cord_=list(zip(cl.region_dict[betti].region_dict[key].polygon.coords.xy))
            print('point: ',i,betti,key,cord,cord_,cl.region_dict[betti].region_dict[key].predict(cord,0.5))           
                
        
result:  0
predict:  True
Polygon:  0 4 0 [1.2000000000000002, 1.25] 1.0454545454545454 1.2307692307692308 1.25 1.2500000000000004 True
Polygon:  1 3 0 [1.1500000000000001, 1.1999999999999997] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  1 3 1 [1.1500000000000001, 1.1999999999999997] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  1 3 2 [1.1500000000000001, 1.1999999999999997] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  2 3 0 [1.1500000000000001, 1.166666666666667] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  2 3 1 [1.1500000000000001, 1.166666666666667] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  2 3 2 [1.1500000000000001, 1.166666666666667] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  3 3 0 [1.1500000000000001, 1.166666666666667] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  3 3 1 [1.1500000000000001, 1.166666666666667] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  3 3 2 [1.1500000000000001, 1.166666666666667] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  4 3 0 [1.15, 1.1666666666666667] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  4 3 1 [1.15, 1.1666666666666667] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  4 3 2 [1.15, 1.1666666666666667] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  5 3 0 [1.1500000000000004, 1.166666666666667] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  5 3 1 [1.1500000000000004, 1.166666666666667] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  5 3 2 [1.1500000000000004, 1.166666666666667] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  6 3 0 [1.1500000000000001, 1.1666666666666667] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  6 3 1 [1.1500000000000001, 1.1666666666666667] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  6 3 2 [1.1500000000000001, 1.1666666666666667] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  7 3 0 [1.1500000000000001, 1.1666666666666665] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  7 3 1 [1.1500000000000001, 1.1666666666666665] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  7 3 2 [1.1500000000000001, 1.1666666666666665] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  8 3 0 [1.1500000000000001, 1.1666666666666665] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  8 3 1 [1.1500000000000001, 1.1666666666666665] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  8 3 2 [1.1500000000000001, 1.1666666666666665] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  9 3 0 [1.1500000000000001, 1.1666666666666665] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  9 3 1 [1.1500000000000001, 1.1666666666666665] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  9 3 2 [1.1500000000000001, 1.1666666666666665] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  10 3 0 [1.1500000000000004, 1.1666666666666667] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  10 3 1 [1.1500000000000004, 1.1666666666666667] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  10 3 2 [1.1500000000000004, 1.1666666666666667] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  11 3 0 [1.1500000000000004, 1.1666666666666665] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  11 3 1 [1.1500000000000004, 1.1666666666666665] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  11 3 2 [1.1500000000000004, 1.1666666666666665] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  12 3 0 [1.1666666666666665, 1.166666666666667] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  12 3 1 [1.1666666666666665, 1.166666666666667] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  12 3 2 [1.1666666666666665, 1.166666666666667] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  13 3 0 [1.1666666666666665, 1.1666666666666667] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  13 3 1 [1.1666666666666665, 1.1666666666666667] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  13 3 2 [1.1666666666666665, 1.1666666666666667] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  14 3 0 [1.0, 1.0000000000000002] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  14 3 1 [1.0, 1.0000000000000002] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  14 3 2 [1.0, 1.0000000000000002] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  15 3 0 [1.1666666666666665, 1.1666666666666667] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  15 3 1 [1.1666666666666665, 1.1666666666666667] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  15 3 2 [1.1666666666666665, 1.1666666666666667] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  16 3 0 [1.1666666666666665, 1.1666666666666667] 0.75 1.2115384615384617 0.8 1.2307692307692308 True
Polygon:  16 3 1 [1.1666666666666665, 1.1666666666666667] 3.0 3.166666666666667 3.0000000000000004 3.1666666666666674 False
Polygon:  16 3 2 [1.1666666666666665, 1.1666666666666667] 1.75 1.9166666666666667 1.7500000000000004 1.916666666666667 False
Polygon:  17 2 0 [1.125, 1.1500000000000001] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  18 2 0 [1.125, 1.1500000000000001] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  19 2 0 [1.125, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  20 2 0 [1.125, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  21 2 0 [1.125, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  22 2 0 [1.125, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  23 2 0 [1.125, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  24 2 0 [1.125, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  25 2 0 [1.125, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  26 2 0 [1.1428571428571428, 1.1500000000000001] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  27 2 0 [1.1428571428571428, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  28 2 0 [1.1428571428571428, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  29 2 0 [1.1428571428571428, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  30 2 0 [1.1428571428571428, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  31 2 0 [1.1428571428571428, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  32 2 0 [1.1428571428571428, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  33 2 0 [1.1428571428571428, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  34 2 0 [1.1428571428571428, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  35 2 0 [1.142857142857143, 1.15] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  36 2 0 [0.75, 0.7500000000000001] 0.6666666666666665 1.1666666666666665 0.75 1.2000000000000002 True
Polygon:  37 1 0 [66576448.75, 72869183.93970641] 0.25 1.0 0.5 1.125 False
Polygon:  38 1 0 [27671443.5, 28879867.882560924] 0.25 1.0 0.5 1.125 False
Polygon:  39 1 0 [40759149.25, 41629503.4893313] 0.25 1.0 0.5 1.125 False
Polygon:  40 1 0 [54937185.25, 55491705.83280003] 0.25 1.0 0.5 1.125 False
Polygon:  41 1 0 [69969881.5, 70482371.35598415] 0.25 1.0 0.5 1.125 False
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Line:  82 0 7 [0.0, 0.25] [0.0, 2.25] [0.0, 3.25] False
Line:  82 0 8 [0.0, 0.25] [0.0, 8.5] [0.0, 8.75] False
point:  82 0 9 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [137.5]),)] False
point:  82 0 10 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [16.5]),)] False
Line:  82 0 11 [0.0, 0.25] [0.0, 252.0] [0.0, 252.5] False
Line:  82 0 12 [0.0, 0.25] [0.0, 26.75] [0.0, 27.5] False
Line:  82 0 13 [0.0, 0.25] [0.0, 6.5] [0.0, 7.5] False
point:  82 0 14 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [414.25]),)] False
Line:  82 0 15 [0.0, 0.25] [0.0, 38.75] [0.0, 39.0] False
point:  82 0 16 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [83.75]),)] False
point:  82 0 17 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [75.5]),)] False
point:  82 0 18 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [60.5]),)] False
point:  82 0 19 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [32.25]),)] False
point:  82 0 20 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [242.5]),)] False
point:  82 0 21 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [134.25]),)] False
point:  82 0 22 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [13.25]),)] False
Line:  82 0 23 [0.0, 0.25] [0.0, 534.5] [0.0, 535.0] False
point:  82 0 24 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [348.5]),)] False
point:  82 0 25 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [57.75]),)] False
point:  82 0 26 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [125.0]),)] False
point:  82 0 27 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [14.75]),)] False
point:  82 0 28 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [194.25]),)] False
point:  82 0 29 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [25.25]),)] False
Line:  82 0 30 [0.0, 0.25] [0.0, 864.0] [0.0, 864.5] False
point:  82 0 31 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [288.0]),)] False
Line:  82 0 32 [0.0, 0.25] [0.0, 114.25] [0.0, 114.75] False
point:  82 0 33 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [144.0]),)] False
point:  82 0 34 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [258.5]),)] False
point:  82 0 35 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [33.5]),)] False
Line:  82 0 36 [0.0, 0.25] [0.0, 36.0] [0.0, 36.25] False
point:  82 0 37 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [17.25]),)] False
point:  82 0 38 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [51.0]),)] False
point:  82 0 39 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [148.75]),)] False
Line:  82 0 40 [0.0, 0.25] [0.0, 10.0] [0.0, 10.25] False
point:  82 0 41 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [223.5]),)] False
point:  82 0 42 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [216.0]),)] False
point:  82 0 43 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [108.0]),)] False
point:  82 0 44 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [65.0]),)] False
point:  82 0 45 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [72.0]),)] False
point:  82 0 46 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [524.75]),)] False
Line:  82 0 47 [0.0, 0.25] [0.0, 1007.75] [0.0, 1008.5] False
point:  83 0 0 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [1.7976931348623157e+278]),)] False
point:  83 0 1 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [164.25]),)] False
Line:  83 0 2 [0.0, 0.25] [0.0, 20.25] [0.0, 21.0] False
Line:  83 0 3 [0.0, 0.25] [0.0, 4.0] [0.0, 5.25] False
Line:  83 0 4 [0.0, 0.25] [0.0, 0.25] [0.0, 1.25] True
Line:  83 0 5 [0.0, 0.25] [0.0, 67.5] [0.0, 67.75] False
Line:  83 0 6 [0.0, 0.25] [0.0, 11.25] [0.0, 11.75] False
Line:  83 0 7 [0.0, 0.25] [0.0, 2.25] [0.0, 3.25] False
Line:  83 0 8 [0.0, 0.25] [0.0, 8.5] [0.0, 8.75] False
point:  83 0 9 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [137.5]),)] False
point:  83 0 10 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [16.5]),)] False
Line:  83 0 11 [0.0, 0.25] [0.0, 252.0] [0.0, 252.5] False
Line:  83 0 12 [0.0, 0.25] [0.0, 26.75] [0.0, 27.5] False
Line:  83 0 13 [0.0, 0.25] [0.0, 6.5] [0.0, 7.5] False
point:  83 0 14 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [414.25]),)] False
Line:  83 0 15 [0.0, 0.25] [0.0, 38.75] [0.0, 39.0] False
point:  83 0 16 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [83.75]),)] False
point:  83 0 17 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [75.5]),)] False
point:  83 0 18 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [60.5]),)] False
point:  83 0 19 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [32.25]),)] False
point:  83 0 20 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [242.5]),)] False
point:  83 0 21 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [134.25]),)] False
point:  83 0 22 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [13.25]),)] False
Line:  83 0 23 [0.0, 0.25] [0.0, 534.5] [0.0, 535.0] False
point:  83 0 24 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [348.5]),)] False
point:  83 0 25 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [57.75]),)] False
point:  83 0 26 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [125.0]),)] False
point:  83 0 27 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [14.75]),)] False
point:  83 0 28 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [194.25]),)] False
point:  83 0 29 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [25.25]),)] False
Line:  83 0 30 [0.0, 0.25] [0.0, 864.0] [0.0, 864.5] False
point:  83 0 31 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [288.0]),)] False
Line:  83 0 32 [0.0, 0.25] [0.0, 114.25] [0.0, 114.75] False
point:  83 0 33 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [144.0]),)] False
point:  83 0 34 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [258.5]),)] False
point:  83 0 35 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [33.5]),)] False
Line:  83 0 36 [0.0, 0.25] [0.0, 36.0] [0.0, 36.25] False
point:  83 0 37 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [17.25]),)] False
point:  83 0 38 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [51.0]),)] False
point:  83 0 39 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [148.75]),)] False
Line:  83 0 40 [0.0, 0.25] [0.0, 10.0] [0.0, 10.25] False
point:  83 0 41 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [223.5]),)] False
point:  83 0 42 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [216.0]),)] False
point:  83 0 43 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [108.0]),)] False
point:  83 0 44 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [65.0]),)] False
point:  83 0 45 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [72.0]),)] False
point:  83 0 46 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [524.75]),)] False
Line:  83 0 47 [0.0, 0.25] [0.0, 1007.75] [0.0, 1008.5] False
point:  84 0 0 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [1.7976931348623157e+278]),)] False
point:  84 0 1 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [164.25]),)] False
Line:  84 0 2 [0.0, 0.25] [0.0, 20.25] [0.0, 21.0] False
Line:  84 0 3 [0.0, 0.25] [0.0, 4.0] [0.0, 5.25] False
Line:  84 0 4 [0.0, 0.25] [0.0, 0.25] [0.0, 1.25] True
Line:  84 0 5 [0.0, 0.25] [0.0, 67.5] [0.0, 67.75] False
Line:  84 0 6 [0.0, 0.25] [0.0, 11.25] [0.0, 11.75] False
Line:  84 0 7 [0.0, 0.25] [0.0, 2.25] [0.0, 3.25] False
Line:  84 0 8 [0.0, 0.25] [0.0, 8.5] [0.0, 8.75] False
point:  84 0 9 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [137.5]),)] False
point:  84 0 10 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [16.5]),)] False
Line:  84 0 11 [0.0, 0.25] [0.0, 252.0] [0.0, 252.5] False
Line:  84 0 12 [0.0, 0.25] [0.0, 26.75] [0.0, 27.5] False
Line:  84 0 13 [0.0, 0.25] [0.0, 6.5] [0.0, 7.5] False
point:  84 0 14 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [414.25]),)] False
Line:  84 0 15 [0.0, 0.25] [0.0, 38.75] [0.0, 39.0] False
point:  84 0 16 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [83.75]),)] False
point:  84 0 17 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [75.5]),)] False
point:  84 0 18 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [60.5]),)] False
point:  84 0 19 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [32.25]),)] False
point:  84 0 20 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [242.5]),)] False
point:  84 0 21 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [134.25]),)] False
point:  84 0 22 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [13.25]),)] False
Line:  84 0 23 [0.0, 0.25] [0.0, 534.5] [0.0, 535.0] False
point:  84 0 24 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [348.5]),)] False
point:  84 0 25 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [57.75]),)] False
point:  84 0 26 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [125.0]),)] False
point:  84 0 27 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [14.75]),)] False
point:  84 0 28 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [194.25]),)] False
point:  84 0 29 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [25.25]),)] False
Line:  84 0 30 [0.0, 0.25] [0.0, 864.0] [0.0, 864.5] False
point:  84 0 31 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [288.0]),)] False
Line:  84 0 32 [0.0, 0.25] [0.0, 114.25] [0.0, 114.75] False
point:  84 0 33 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [144.0]),)] False
point:  84 0 34 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [258.5]),)] False
point:  84 0 35 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [33.5]),)] False
Line:  84 0 36 [0.0, 0.25] [0.0, 36.0] [0.0, 36.25] False
point:  84 0 37 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [17.25]),)] False
point:  84 0 38 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [51.0]),)] False
point:  84 0 39 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [148.75]),)] False
Line:  84 0 40 [0.0, 0.25] [0.0, 10.0] [0.0, 10.25] False
point:  84 0 41 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [223.5]),)] False
point:  84 0 42 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [216.0]),)] False
point:  84 0 43 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [108.0]),)] False
point:  84 0 44 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [65.0]),)] False
point:  84 0 45 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [72.0]),)] False
point:  84 0 46 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [524.75]),)] False
Line:  84 0 47 [0.0, 0.25] [0.0, 1007.75] [0.0, 1008.5] False
point:  85 0 0 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [1.7976931348623157e+278]),)] False
point:  85 0 1 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [164.25]),)] False
Line:  85 0 2 [0.0, 0.25] [0.0, 20.25] [0.0, 21.0] False
Line:  85 0 3 [0.0, 0.25] [0.0, 4.0] [0.0, 5.25] False
Line:  85 0 4 [0.0, 0.25] [0.0, 0.25] [0.0, 1.25] True
Line:  85 0 5 [0.0, 0.25] [0.0, 67.5] [0.0, 67.75] False
Line:  85 0 6 [0.0, 0.25] [0.0, 11.25] [0.0, 11.75] False
Line:  85 0 7 [0.0, 0.25] [0.0, 2.25] [0.0, 3.25] False
Line:  85 0 8 [0.0, 0.25] [0.0, 8.5] [0.0, 8.75] False
point:  85 0 9 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [137.5]),)] False
point:  85 0 10 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [16.5]),)] False
Line:  85 0 11 [0.0, 0.25] [0.0, 252.0] [0.0, 252.5] False
Line:  85 0 12 [0.0, 0.25] [0.0, 26.75] [0.0, 27.5] False
Line:  85 0 13 [0.0, 0.25] [0.0, 6.5] [0.0, 7.5] False
point:  85 0 14 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [414.25]),)] False
Line:  85 0 15 [0.0, 0.25] [0.0, 38.75] [0.0, 39.0] False
point:  85 0 16 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [83.75]),)] False
point:  85 0 17 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [75.5]),)] False
point:  85 0 18 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [60.5]),)] False
point:  85 0 19 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [32.25]),)] False
point:  85 0 20 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [242.5]),)] False
point:  85 0 21 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [134.25]),)] False
point:  85 0 22 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [13.25]),)] False
Line:  85 0 23 [0.0, 0.25] [0.0, 534.5] [0.0, 535.0] False
point:  85 0 24 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [348.5]),)] False
point:  85 0 25 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [57.75]),)] False
point:  85 0 26 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [125.0]),)] False
point:  85 0 27 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [14.75]),)] False
point:  85 0 28 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [194.25]),)] False
point:  85 0 29 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [25.25]),)] False
Line:  85 0 30 [0.0, 0.25] [0.0, 864.0] [0.0, 864.5] False
point:  85 0 31 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [288.0]),)] False
Line:  85 0 32 [0.0, 0.25] [0.0, 114.25] [0.0, 114.75] False
point:  85 0 33 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [144.0]),)] False
point:  85 0 34 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [258.5]),)] False
point:  85 0 35 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [33.5]),)] False
Line:  85 0 36 [0.0, 0.25] [0.0, 36.0] [0.0, 36.25] False
point:  85 0 37 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [17.25]),)] False
point:  85 0 38 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [51.0]),)] False
point:  85 0 39 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [148.75]),)] False
Line:  85 0 40 [0.0, 0.25] [0.0, 10.0] [0.0, 10.25] False
point:  85 0 41 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [223.5]),)] False
point:  85 0 42 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [216.0]),)] False
point:  85 0 43 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [108.0]),)] False
point:  85 0 44 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [65.0]),)] False
point:  85 0 45 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [72.0]),)] False
point:  85 0 46 [0.0, 0.25] [(array('d', [0.0]),), (array('d', [524.75]),)] False
Line:  85 0 47 [0.0, 0.25] [0.0, 1007.75] [0.0, 1008.5] False
num=1
print('predict: ',cl.predict(test_list[num]),test_label_list[num])
for i, cor in enumerate(test_list[num]):
    betti, cord=cor
    print(i,betti,cl.region_dict[betti].predict([cord]))
result:  0
predict:  True 0
0 4 True
1 3 True
2 3 True
3 3 True
4 3 True
5 3 True
6 3 True
7 3 True
8 3 True
9 3 True
10 3 True
11 3 True
12 3 True
13 3 True
14 3 True
15 3 True
16 3 True
17 3 True
18 3 True
19 3 True
20 3 True
21 2 True
22 2 True
23 2 True
24 2 True
25 2 True
26 2 True
27 2 True
28 2 True
29 2 True
30 2 True
31 2 True
32 2 True
33 2 True
34 2 True
35 2 True
36 2 True
37 2 True
38 2 True
39 2 True
40 2 True
41 1 False
42 1 False
43 1 False
44 1 False
45 1 False
46 1 False
47 1 True
48 0 False
49 0 False
50 0 False
51 0 False
52 0 False
53 0 False
54 0 False
55 0 False
56 0 False
57 0 False
58 0 False
59 0 False
60 0 False
61 0 False
62 0 False
63 0 False
64 0 False
65 0 False
66 0 False
67 0 False
68 0 False
69 0 False
70 0 False
71 0 False
72 0 False
73 0 False
74 0 False
75 0 False
76 0 False
77 0 False
78 0 False
79 0 False
80 0 False
81 0 False
82 0 False
83 0 False
84 0 False
85 0 False
86 0 False
87 0 False
True
cl.region_dict[4].region_dict[0].polygon.exterior.coords.xy
(array('d', [1.1666666666666667, 1.1666666666666667, 1.2307692307692308, 1.2307692307692308, 1.1666666666666667]),
 array('d', [1.25, 1.2500000000000004, 1.2500000000000004, 1.25, 1.25]))
cl.region_dict[4].region_dict[0].predict(test_dict['good'][0][0][1],0.5)
False
sh.geometry.point.Point(test_dict['good'][0][0][1]).within(cl.region_dict[4].region_dict[0].polygon)
False
test_dict['good'][0][0][1]
[1.2000000000000002, 1.25]
tmp=sh.geometry.polygon.Polygon([[0,0],[0,1],[1,1],[1,0]])
p1=sh.geometry.point.Point([0,0.5])
p1.contains(tmp)
False
geometry=sh.geometry
polygon = [(3, 2), (5, 1), (7, 2), (8, 6), (9, 7), (11, 6), (10, 4), (12, 2), (15, 2), (16, 5), (19, 7), (18, 11), (14, 12), (11, 9), (5, 9), (2, 6)] 
Point_X = 14
Point_Y = 12

line = geometry.LineString(polygon)
point = geometry.Point(Point_X, Point_Y)

print(line.contains(point))
True
np.array(a).min()
0.0
result=metrics.classification_report(test_label_list,predict_label_list)
print(result)
              precision    recall  f1-score   support

           0       0.00      0.00      0.00        82
           1       0.34      0.96      0.51        45

    accuracy                           0.34       127
   macro avg       0.17      0.48      0.25       127
weighted avg       0.12      0.34      0.18       127
for i,test_label in enumerate(test_label_list):
    if test_label!=predict_label_list[i]:
        print(i,test_label,predict_label_list[i])
12 0 True
52 0 True
68 0 True
82 1 False
83 1 False
84 1 False
85 1 False
86 1 False
87 1 False
88 1 False
89 1 False
90 1 False
91 1 False
92 1 False
93 1 False
94 1 False
95 1 False
96 1 False
97 1 False
98 1 False
99 1 False
100 1 False
101 1 False
102 1 False
103 1 False
104 1 False
105 1 False
106 1 False
107 1 False
108 1 False
109 1 False
110 1 False
111 1 False
112 1 False
113 1 False
114 1 False
115 1 False
116 1 False
117 1 False
118 1 False
119 1 False
120 1 False
121 1 False
122 1 False
123 1 False
124 1 False
125 1 False
126 1 False
len(test_list)
127
class tmp:
    def __init__(self,content):
        self.list=content
    def __eq__(self,_blist):
        if len(self.list)!=len(_blist):
            return False
        result=[]
        for i in range(len(self.list)):
            result.append(self.list[i]==_blist[i])
        mul=1
        for i in result:
            mul*=i
        return i
    def __len__(self):
        
        return len(self.list)
    def __getitem__(self,i):
        return self.list[i]
    
            
import numpy as np
a=np.array([1,1])
b=np.array([1,1])
print(set([a,b]))
---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-42-5b751d3ad4e8> in <module>
      2 a=np.array([1,1])
      3 b=np.array([1,1])
----> 4 print(set([a,b]))


TypeError: unhashable type: 'numpy.ndarray'
bc=gudhi.representations.vector_methods.BettiCurve()
for i, name in enumerate(file_list):
    print(i,name)
0 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.1.1.txt
1 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.1.2.txt
2 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.1.3.txt
3 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.1.5.txt
4 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.2.1.txt
5 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.2.2.txt
6 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.2.3.txt
7 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.2.5.txt
8 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.3.1.txt
9 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.3.2.txt
10 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.3.3.txt
11 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.3.5.txt
12 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.5.1.txt
13 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.5.2.txt
14 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.5.3.txt
15 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.5.5.txt
16 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.1.1.txt
17 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.1.2.txt
18 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.1.3.txt
19 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.1.5.txt
20 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.2.1.txt
21 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.2.2.txt
22 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.2.3.txt
23 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.2.5.txt
24 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.3.1.txt
25 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.3.2.txt
26 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.3.3.txt
27 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.3.5.txt
28 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.5.1.txt
29 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.5.2.txt
30 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.5.3.txt
31 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.5.5.txt
32 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.1.1.txt
33 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.1.2.txt
34 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.1.3.txt
35 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.1.5.txt
36 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.2.1.txt
37 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.2.2.txt
38 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.2.3.txt
39 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.2.5.txt
40 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.3.1.txt
41 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.3.2.txt
42 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.3.3.txt
43 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.3.5.txt
44 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.5.1.txt
45 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.5.2.txt
46 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.5.3.txt
47 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.5.5.txt
48 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.1.1.txt
49 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.1.2.txt
50 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.1.3.txt
51 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.1.5.txt
52 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.2.1.txt
53 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.2.2.txt
54 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.2.3.txt
55 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.2.5.txt
56 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.3.1.txt
57 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.3.2.txt
58 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.3.3.txt
59 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.3.5.txt
60 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.5.1.txt
61 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.5.2.txt
62 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.5.3.txt
63 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.5.5.txt
64 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.1.1.txt
65 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.1.2.txt
66 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.1.3.txt
67 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.1.5.txt
68 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.2.1.txt
69 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.2.2.txt
70 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.2.3.txt
71 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.2.5.txt
72 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.3.1.txt
73 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.3.2.txt
74 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.3.3.txt
75 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.3.5.txt
76 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.5.1.txt
77 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.5.2.txt
78 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.5.3.txt
79 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.5.5.txt
80 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.1.1.txt
81 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.1.2.txt
82 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.1.3.txt
83 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.1.5.txt
84 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.2.1.txt
85 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.2.2.txt
86 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.2.3.txt
87 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.2.5.txt
88 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.3.1.txt
89 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.3.2.txt
90 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.3.3.txt
91 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.3.5.txt
92 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.5.1.txt
93 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.5.2.txt
94 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.5.3.txt
95 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.5.5.txt
96 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.1.1.txt
97 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.1.2.txt
98 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.1.3.txt
99 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.1.5.txt
100 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.2.1.txt
101 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.2.2.txt
102 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.2.3.txt
103 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.2.5.txt
104 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.3.1.txt
105 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.3.2.txt
106 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.3.3.txt
107 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.3.5.txt
108 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.5.1.txt
109 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.5.2.txt
110 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.5.3.txt
111 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.5.5.txt
112 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.1.1.txt
113 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.1.2.txt
114 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.1.3.txt
115 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.1.5.txt
116 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.2.1.txt
117 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.2.2.txt
118 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.2.3.txt
119 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.2.5.txt
120 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.3.1.txt
121 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.3.2.txt
122 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.3.3.txt
123 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.3.5.txt
124 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.5.1.txt
125 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.5.2.txt
126 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.5.3.txt
127 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.5.5.txt
data_dict['good'][0]
tmp=[]
for i, cord in data_dict['good'][0]:
    tmp.append([cord])
reload(gudhi)
<module 'gudhi' from 'D:\\PROJECT\\seminconductor\\code\\lib\\site-packages\\gudhi\\__init__.py'>
import os
import gudhi
import json
import numpy as np
def get_persistence_dict(file_name):
    with open(file_name,'r') as f:
        data_dict=json.load(f)
    return data_dict

basic_work_path=os.path.join(os.getcwd(),'result','embedding')
complex_list=os.listdir(basic_work_path)
file_list=[]
for complex_ in complex_list:
    work_path=os.path.join(basic_work_path,complex_)
    file_list.append(os.path.join(work_path))
data_dict=get_persistence_dict(file_list[0])
complex_=gudhi.AlphaComplex(data_dict['good'][0])
tree=complex_.create_simplex_tree()
persistence=tree.persistence()
bc=gudhi.representations.vector_methods.BettiCurve()
bc(persistence)
---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

TypeError: float() argument must be a string or a number, not 'tuple'


The above exception was the direct cause of the following exception:


ValueError                                Traceback (most recent call last)

<ipython-input-5-8480c6c3bcc3> in <module>
     19 persistence=tree.persistence()
     20 bc=gudhi.representations.vector_methods.BettiCurve()
---> 21 bc(persistence)


D:\PROJECT\seminconductor\code\lib\site-packages\gudhi\representations\vector_methods.py in __call__(self, diag)
    357             numpy array with shape (**resolution**): output Betti curve.
    358         """
--> 359         return self.fit_transform([diag])[0,:]
    360 
    361 class Entropy(BaseEstimator, TransformerMixin):


D:\PROJECT\seminconductor\code\lib\site-packages\sklearn\base.py in fit_transform(self, X, y, **fit_params)
    688         if y is None:
    689             # fit method of arity 1 (unsupervised transformation)
--> 690             return self.fit(X, **fit_params).transform(X)
    691         else:
    692             # fit method of arity 2 (supervised transformation)


D:\PROJECT\seminconductor\code\lib\site-packages\gudhi\representations\vector_methods.py in fit(self, X, y)
    308         """
    309         if np.isnan(np.array(self.sample_range)).any():
--> 310             pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
    311             #pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler), ([1], MinMaxScaler)]).fit(X,y)
    312             #pre = DiagramScaler(use=True, scalers=[(0, MinMaxScaler), (1, MinMaxScaler)]).fit(X,y)


D:\PROJECT\seminconductor\code\lib\site-packages\gudhi\representations\preprocessing.py in fit(self, X, y)
    137                 P =np.array(P)
    138                 ###
--> 139                 scaler.fit(np.reshape(P[:,indices], [-1, 1]))
    140         return self
    141 


D:\PROJECT\seminconductor\code\lib\site-packages\sklearn\preprocessing\_data.py in fit(self, X, y)
    334         # Reset internal state before fitting
    335         self._reset()
--> 336         return self.partial_fit(X, y)
    337 
    338     def partial_fit(self, X, y=None):


D:\PROJECT\seminconductor\code\lib\site-packages\sklearn\preprocessing\_data.py in partial_fit(self, X, y)
    369         X = self._validate_data(X, reset=first_pass,
    370                                 estimator=self, dtype=FLOAT_DTYPES,
--> 371                                 force_all_finite="allow-nan")
    372 
    373         data_min = np.nanmin(X, axis=0)


D:\PROJECT\seminconductor\code\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
    418                     f"requires y to be passed, but the target y is None."
    419                 )
--> 420             X = check_array(X, **check_params)
    421             out = X
    422         else:


D:\PROJECT\seminconductor\code\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     71                           FutureWarning)
     72         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73         return f(**kwargs)
     74     return inner_f
     75 


D:\PROJECT\seminconductor\code\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
    597                     array = array.astype(dtype, casting="unsafe", copy=False)
    598                 else:
--> 599                     array = np.asarray(array, order=order, dtype=dtype)
    600             except ComplexWarning:
    601                 raise ValueError("Complex data not supported\n"


D:\PROJECT\seminconductor\code\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 


ValueError: setting an array element with a sequence.
result=bc(np.array(tmp))
print(len(result))
plt.plot(result)
index:  0
100





[<matplotlib.lines.Line2D at 0x2236c215348>]

png

import matplotlib.pyplot as plt
%matplotlib inline
for i in range(10):
    result=bc(np.array(data_dict['bad'][i+10]))
    plt.plot(result)
index:  0



---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-32-2904dc200c8d> in <module>
      1 for i in range(10):
----> 2     result=bc(np.array(data_dict['bad'][i+10]))
      3     plt.plot(result)


D:\PROJECT\seminconductor\code\lib\site-packages\gudhi\representations\vector_methods.py in __call__(self, diag)
    357         """
    358         return self.fit_transform([diag])[0,:]
--> 359 
    360 class Entropy(BaseEstimator, TransformerMixin):
    361     """


D:\PROJECT\seminconductor\code\lib\site-packages\sklearn\base.py in fit_transform(self, X, y, **fit_params)
    688         if y is None:
    689             # fit method of arity 1 (unsupervised transformation)
--> 690             return self.fit(X, **fit_params).transform(X)
    691         else:
    692             # fit method of arity 2 (supervised transformation)


D:\PROJECT\seminconductor\code\lib\site-packages\gudhi\representations\vector_methods.py in transform(self, X)
    338                 max_idx = np.clip(np.ceil((py - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
    339                 for k in range(min_idx, max_idx):
--> 340                     bc[k] += 1
    341 
    342             Xfit.append(np.reshape(bc,[1,-1]))


TypeError: only integer scalar arrays can be converted to a scalar index
cp=gudhi.representations.ComplexPolynomial()
cp(np.array(data_dict['good'][0]))
---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-36-530e6f76aeca> in <module>
----> 1 cp(np.array(data_dict['good'][0]))


D:\PROJECT\seminconductor\code\lib\site-packages\gudhi\representations\vector_methods.py in __call__(self, diag)
    576         """
    577         return self.fit_transform([diag])[0,:]


D:\PROJECT\seminconductor\code\lib\site-packages\sklearn\base.py in fit_transform(self, X, y, **fit_params)
    688         if y is None:
    689             # fit method of arity 1 (unsupervised transformation)
--> 690             return self.fit(X, **fit_params).transform(X)
    691         else:
    692             # fit method of arity 2 (supervised transformation)


D:\PROJECT\seminconductor\code\lib\site-packages\gudhi\representations\vector_methods.py in transform(self, X)
    549             if self.polynomial_type == "R":
    550                 roots = D[:,0] + 1j * D[:,1]
--> 551             elif self.polynomial_type == "S":
    552                 alpha = np.linalg.norm(D, axis=1)
    553                 alpha = np.where(alpha==0, np.ones(N), alpha)


TypeError: can't multiply sequence by non-int of type 'complex'
for i in range(10):
    pi=gudhi.representations.PersistenceImage()
    result=pi(np.array(data_dict['good'][i]))
    plt.plot(result)
    result=pi(np.array(data_dict['bad'][i]))
    plt.plot(result)

png

plt.plot(result)
[<matplotlib.lines.Line2D at 0x2236c3333c8>]

png

for i, name in enumerate(file_list):
    print(i,name)
0 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.1.1.txt
1 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.1.2.txt
2 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.1.3.txt
3 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.1.5.txt
4 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.2.1.txt
5 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.2.2.txt
6 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.2.3.txt
7 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.2.5.txt
8 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.3.1.txt
9 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.3.2.txt
10 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.3.3.txt
11 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.3.5.txt
12 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.5.1.txt
13 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.5.2.txt
14 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.5.3.txt
15 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_2.5.5.txt
16 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.1.1.txt
17 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.1.2.txt
18 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.1.3.txt
19 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.1.5.txt
20 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.2.1.txt
21 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.2.2.txt
22 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.2.3.txt
23 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.2.5.txt
24 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.3.1.txt
25 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.3.2.txt
26 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.3.3.txt
27 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.3.5.txt
28 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.5.1.txt
29 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.5.2.txt
30 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.5.3.txt
31 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_3.5.5.txt
32 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.1.1.txt
33 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.1.2.txt
34 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.1.3.txt
35 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.1.5.txt
36 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.2.1.txt
37 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.2.2.txt
38 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.2.3.txt
39 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.2.5.txt
40 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.3.1.txt
41 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.3.2.txt
42 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.3.3.txt
43 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.3.5.txt
44 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.5.1.txt
45 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.5.2.txt
46 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.5.3.txt
47 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_5.5.5.txt
48 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.1.1.txt
49 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.1.2.txt
50 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.1.3.txt
51 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.1.5.txt
52 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.2.1.txt
53 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.2.2.txt
54 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.2.3.txt
55 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.2.5.txt
56 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.3.1.txt
57 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.3.2.txt
58 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.3.3.txt
59 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.3.5.txt
60 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.5.1.txt
61 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.5.2.txt
62 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.5.3.txt
63 D:\PROJECT\seminconductor\code\data2\result\complex\alpha\alpha_7.5.5.txt
64 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.1.1.txt
65 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.1.2.txt
66 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.1.3.txt
67 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.1.5.txt
68 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.2.1.txt
69 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.2.2.txt
70 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.2.3.txt
71 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.2.5.txt
72 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.3.1.txt
73 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.3.2.txt
74 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.3.3.txt
75 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.3.5.txt
76 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.5.1.txt
77 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.5.2.txt
78 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.5.3.txt
79 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_2.5.5.txt
80 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.1.1.txt
81 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.1.2.txt
82 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.1.3.txt
83 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.1.5.txt
84 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.2.1.txt
85 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.2.2.txt
86 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.2.3.txt
87 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.2.5.txt
88 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.3.1.txt
89 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.3.2.txt
90 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.3.3.txt
91 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.3.5.txt
92 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.5.1.txt
93 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.5.2.txt
94 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.5.3.txt
95 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_3.5.5.txt
96 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.1.1.txt
97 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.1.2.txt
98 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.1.3.txt
99 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.1.5.txt
100 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.2.1.txt
101 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.2.2.txt
102 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.2.3.txt
103 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.2.5.txt
104 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.3.1.txt
105 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.3.2.txt
106 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.3.3.txt
107 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.3.5.txt
108 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.5.1.txt
109 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.5.2.txt
110 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.5.3.txt
111 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_5.5.5.txt
112 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.1.1.txt
113 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.1.2.txt
114 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.1.3.txt
115 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.1.5.txt
116 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.2.1.txt
117 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.2.2.txt
118 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.2.3.txt
119 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.2.5.txt
120 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.3.1.txt
121 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.3.2.txt
122 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.3.3.txt
123 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.3.5.txt
124 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.5.1.txt
125 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.5.2.txt
126 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.5.3.txt
127 D:\PROJECT\seminconductor\code\data2\result\complex\rips\rips_7.5.5.txt
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 [2, [1.1428571428571428, 1.15]],
 [2, [1.1428571428571428, 1.15]],
 [2, [1.142857142857143, 1.1500000000000001]],
 [2, [1.6071428571428568, 1.6136363636363638]],
 [2, [1.6071428571428572, 1.6136363636363638]],
 [2, [1.6071428571428568, 1.6111111111111112]],
 [2, [1.3888888888888888, 1.392857142857143]],
 [2, [1.3888888888888888, 1.392857142857143]],
 [2, [1.3888888888888888, 1.392857142857143]],
 [2, [1.3888888888888888, 1.3928571428571428]],
 [2, [1.3888888888888888, 1.3928571428571428]],
 [2, [1.3888888888888888, 1.3928571428571428]],
 [2, [1.3888888888888888, 1.3928571428571428]],
 [2, [1.3888888888888888, 1.3928571428571428]],
 [2, [1.3888888888888888, 1.3928571428571428]],
 [2, [1.6071428571428572, 1.6111111111111112]],
 [2, [1.3888888888888888, 1.3928571428571428]],
 [2, [1.3888888888888888, 1.3928571428571428]],
 [2, [1.3888888888888888, 1.3928571428571428]],
 [2, [1.3888888888888888, 1.3928571428571428]],
 [2, [1.3888888888888888, 1.3928571428571428]],
 [2, [1.3888888888888888, 1.3928571428571428]],
 [2, [1.3888888888888888, 1.3928571428571428]],
 [2, [1.3888888888888888, 1.3928571428571428]],
 [2, [1.5625, 1.5657894736842106]],
 [2, [1.5625, 1.5657894736842106]],
 [2, [1.5625, 1.5657894736842104]],
 [2, [1.6363636363636362, 1.6388888888888888]],
 [2, [1.3636363636363638, 1.3653846153846154]],
 [2, [1.3636363636363638, 1.3653846153846154]],
 [2, [1.3636363636363638, 1.3653846153846154]],
 [2, [1.3636363636363635, 1.3653846153846152]],
 [2, [1.25, 1.2500000000000004]],
 [2, [1.25, 1.2500000000000004]],
 [2, [1.25, 1.2500000000000004]],
 [2, [0.75, 0.7500000000000002]],
 [2, [0.75, 0.7500000000000002]],
 [2, [1.0, 1.0000000000000002]],
 [2, [1.125, 1.1250000000000002]],
 [2, [0.75, 0.7500000000000001]],
 [2, [0.75, 0.7500000000000001]],
 [1, [105636194.25, 117689915.63237509]],
 [1, [48169007.25, 52796250.61781386]],
 [1, [72368775.75, 76837198.19309896]],
 [1, [60614815.0, 65082656.98702824]],
 [1, [84060715.0, 88387112.5156396]],
 [1, [97521889.0, 101118518.38313168]],
 [1, [114164160.25, 115602633.7877053]],
 [1, [34913312.25, 35475838.55533149]],
 [1, [32418756.75, 32456970.290059887]],
 [1, [17163173.5, 17177084.324734293]],
 [1, [32514290.0, 32521624.885020055]],
 [1, [0.25, 1.1428571428571428]],
 [1, [0.25, 0.5]],
 [1, [0.25, 0.5]],
 [1, [0.25, 0.5]],
 [1, [0.25, 0.5]],
 [1, [0.25, 0.5]],
 [1, [0.5, 0.6666666666666667]],
 [1, [0.5, 0.6666666666666666]],
 [1, [0.75, 0.9]],
 [1, [0.75, 0.9]],
 [1, [0.75, 0.8999999999999999]],
 [1, [0.75, 0.8999999999999999]],
 [1, [1.0, 1.125]],
 [1, [1.0, 1.125]],
 [1, [0.75, 0.7500000000000002]],
 [0, [0.0, inf]],
 [0, [0.0, 33506994.0]],
 [0, [0.0, 33493916.25]],
 [0, [0.0, 33481696.25]],
 [0, [0.0, 33468736.25]],
 [0, [0.0, 33467718.5]],
 [0, [0.0, 33465695.75]],
 [0, [0.0, 32415551.75]],
 [0, [0.0, 17162961.75]],
 [0, [0.0, 549109.0]],
 [0, [0.0, 166902.0]],
 [0, [0.0, 165000.75]],
 [0, [0.0, 163992.0]],
 [0, [0.0, 159004.5]],
 [0, [0.0, 151910.25]],
 [0, [0.0, 147832.5]],
 [0, [0.0, 138839.5]],
 [0, [0.0, 133768.25]],
 [0, [0.0, 117863.25]],
 [0, [0.0, 115071.0]],
 [0, [0.0, 109511.25]],
 [0, [0.0, 97687.5]],
 [0, [0.0, 86572.25]],
 [0, [0.0, 77929.5]],
 [0, [0.0, 62849.0]],
 [0, [0.0, 56020.25]],
 [0, [0.0, 2175.0]],
 [0, [0.0, 368.75]],
 [0, [0.0, 44.75]],
 [0, [0.0, 8.75]],
 [0, [0.0, 2.75]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]],
 [0, [0.0, 0.25]]]
tmp=[]
for _, cord in data_dict['good'][0]:
    tmp.append(cord)
bc(np.array(cord))
index:  0



---------------------------------------------------------------------------

IndexError                                Traceback (most recent call last)

<ipython-input-40-9a194a6dc9fa> in <module>
----> 1 bc(np.array(cord))


D:\PROJECT\seminconductor\code\lib\site-packages\gudhi\representations\vector_methods.py in __call__(self, diag)
    357         """
    358         return self.fit_transform([diag])[0,:]
--> 359 
    360 class Entropy(BaseEstimator, TransformerMixin):
    361     """


D:\PROJECT\seminconductor\code\lib\site-packages\sklearn\base.py in fit_transform(self, X, y, **fit_params)
    688         if y is None:
    689             # fit method of arity 1 (unsupervised transformation)
--> 690             return self.fit(X, **fit_params).transform(X)
    691         else:
    692             # fit method of arity 2 (supervised transformation)


D:\PROJECT\seminconductor\code\lib\site-packages\gudhi\representations\vector_methods.py in transform(self, X)
    335             for j in range(num_pts_in_diag):
    336                 [px,py] = diagram[j,:2]
--> 337                 min_idx = np.clip(np.ceil((px - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
    338                 max_idx = np.clip(np.ceil((py - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
    339                 for k in range(min_idx, max_idx):


IndexError: too many indices for array
tmp_dict={}
for key, cord in data_dict['good'][0]:
    if key not in tmp_dict.keys():
        tmp_dict[key]=[]
    tmp_dict[key].append(cord)
for key in tmp_dict.keys():
    result=bc(np.array(tmp_dict[key]))
    plt.plot(result,label=str(key))
plt.legend() 
index:  0
index:  0
index:  0
index:  0
index:  0
index:  0
index:  0





<matplotlib.legend.Legend at 0x2236b0a1208>

png