├── Generated_accuracy_table.dat ├── Generated_error_table.dat ├── README.md ├── bcplot.eps ├── mypart1.py ├── mypart2.py └── plrx.txt /Generated_accuracy_table.dat: -------------------------------------------------------------------------------- 1 | 74.647887 69.090909 40.000000 2 | 75.555556 68.131868 50.000000 3 | 75.000000 65.753425 60.000000 4 | 75.396825 63.636364 70.000000 5 | 75.000000 56.756757 80.000000 6 | 74.074074 47.368421 90.000000 7 | -------------------------------------------------------------------------------- /Generated_error_table.dat: -------------------------------------------------------------------------------- 1 | 0.746479 0.690909 40.000000 2 | 0.755556 0.681319 50.000000 3 | 0.750000 0.657534 60.000000 4 | 0.753968 0.636364 70.000000 5 | 0.750000 0.567568 80.000000 6 | 0.740741 0.473684 90.000000 7 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # EEG-Data-predection 2 | The main idea is solve the classification problem using the support vector machine. 3 | The input data for training, testing and validating is taken form the https://archive.ics.uci.edu/ml/datasets/EEG+Database 4 | 5 | The accuracy and error have been calculated and plotted. 6 | -------------------------------------------------------------------------------- /bcplot.eps: -------------------------------------------------------------------------------- 1 | %!PS-Adobe-3.0 EPSF-3.0 2 | %%Title: /home/jravi/proj_itk/bcplot.eps 3 | %%Creator: matplotlib version 1.5.0rc2+57.gfe6da77, http://matplotlib.org/ 4 | %%CreationDate: Mon Nov 16 11:11:26 2015 5 | %%Orientation: portrait 6 | %%BoundingBox: 18 180 594 612 7 | %%EndComments 8 | %%BeginProlog 9 | /mpldict 8 dict def 10 | mpldict begin 11 | /m { moveto } bind def 12 | /l { lineto } bind def 13 | /r { rlineto } bind def 14 | /c { curveto } bind def 15 | /cl { closepath } bind def 16 | /box { 17 | m 18 | 1 index 0 r 19 | 0 exch r 20 | neg 0 r 21 | cl 22 | } bind def 23 | /clipbox { 24 | box 25 | clip 26 | newpath 27 | } bind def 28 | %!PS-Adobe-3.0 Resource-Font 29 | %%Title: Bitstream Vera Sans 30 | %%Copyright: Copyright (c) 2003 by Bitstream, Inc. 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def read_features(par_filename): 9 | vl = [] 10 | with open(par_filename,"r") as file_lines: 11 | #features = [[float(i) for i in line.split()] for line in file_lines] 12 | for line in file_lines: 13 | vl.append(line.split()) 14 | file_lines.close() 15 | for r in vl: 16 | del r[12] 17 | return vl; 18 | 19 | # Function to read the lables from file 20 | def read_labels(par_filename): 21 | vl = [] 22 | with open(par_filename,"r") as file_lines: 23 | for line in file_lines: 24 | vl.append(line.split()) 25 | file_lines.close() 26 | ll = [] 27 | for r in vl: 28 | ll.append(r[12]) 29 | return ll; 30 | 31 | # Function to compute the classification using SVM 32 | def compute_SVC(train_f,train_l): 33 | C=1.0 34 | cache_size=200 35 | class_weight=None 36 | coef0=0.0 37 | decision_function_shape=None 38 | degree=3 39 | gamma='auto' 40 | kernel='rbf' 41 | max_iter=-1 42 | probability=False 43 | random_state=None 44 | shrinking=True 45 | tol=0.001 46 | verbose=False 47 | c = svm.SVC(kernel='linear') 48 | c.fit(train_f,train_l) 49 | return c; 50 | 51 | # Function to calculate the accuracy 52 | def compute_accuracy(test_f,test_l,c): 53 | pred = c.predict(test_f) 54 | print pred 55 | pred_accu = accuracy_score(test_l,pred) 56 | return pred_accu; 57 | 58 | # Function to compute the confusion matrix 59 | def compute_confusion_matrix(test_f,test_l,c): 60 | pred = c.predict(test_f) 61 | x = confusion_matrix(test_l,pred) 62 | return x; 63 | 64 | # Starting of the flow of program 65 | read_data_features_train = read_features("plrx.txt"); 66 | read_data_labels_train = read_labels("plrx.txt"); 67 | model_svc = compute_SVC(read_data_features_train,read_data_labels_train); 68 | accu_percent = compute_accuracy(read_data_features_train,read_data_labels_train,model_svc)*100; 69 | print "Accuracy obtained over the whole training set is %0.6f %% ." % (accu_percent) 70 | #conf_mat = compute_confusion_matrix(read_data_features_train,read_data_labels_train,model_svc); 71 | #print conf_mat 72 | 73 | -------------------------------------------------------------------------------- /mypart2.py: -------------------------------------------------------------------------------- 1 | import numpy as n 2 | from sklearn import svm 3 | from sklearn.metrics import accuracy_score 4 | from sklearn.metrics import confusion_matrix 5 | import matplotlib.pyplot as graph 6 | 7 | # Function to read the data from file 8 | def read_data(par_filename): 9 | vl = [] 10 | with open(par_filename,"r") as file_lines: 11 | for line in file_lines: 12 | vl.append(map(float,line.split())) 13 | file_lines.close() 14 | return vl; 15 | 16 | # Function to read the lables from file 17 | def read_labels(vl): 18 | ll = [] 19 | for r in vl: 20 | ll.append(int(r[12])) 21 | return ll; 22 | 23 | # Function to read the features from file 24 | def read_features(vl): 25 | lp = vl 26 | for r in lp: 27 | r.remove(r[12]) 28 | return lp; 29 | 30 | # Function to compute the classification using SVM 31 | def compute_SVC(train_f,train_l): 32 | C=1.0 33 | cache_size=200 34 | class_weight=None 35 | coef0=0.0 36 | decision_function_shape=None 37 | degree=3 38 | gamma='auto' 39 | kernel='rbf' 40 | max_iter=-1 41 | probability=False 42 | random_state=None 43 | shrinking=True 44 | tol=0.001 45 | verbose=False 46 | c = svm.SVC(kernel='linear') 47 | c.fit(train_f,train_l) 48 | return c; 49 | 50 | # Function to calculate the accuracy 51 | def compute_accuracy(test_f,test_l,c): 52 | pred = c.predict(test_f) 53 | #print pred 54 | pred_accu = accuracy_score(test_l,pred) 55 | return pred_accu; 56 | 57 | # Function to compute the confusion matrix 58 | def compute_confusion_matrix(test_f,test_l,c): 59 | pred = c.predict(test_f) 60 | x = confusion_matrix(test_l,pred) 61 | return x; 62 | 63 | # Function to compute the error 64 | def compute_error(t_f,t_l,c): 65 | err = c.score(t_f,t_l) 66 | return err; 67 | 68 | # Function to split the data based on percentage 69 | def split_data(f,percent): 70 | tot = len(f) 71 | req_xt = int((float(percent)/100)*(tot)) 72 | req_yt = tot - req_xt 73 | xt_get = [] 74 | for s in range(0,(req_xt-1)): 75 | xt_get.append(f[s]) 76 | yt_get = [] 77 | for d in range(req_xt,tot): 78 | yt_get.append(f[d]) 79 | xyt = [] 80 | xyt.append(xt_get) 81 | xyt.append(yt_get) 82 | return xyt; 83 | 84 | # Function to plot the training and testing errors 85 | def compute_plot(filename): 86 | test_plt = [] 87 | train_plt = [] 88 | percent_plt = [] 89 | with open(filename,"r") as lines_in_file: 90 | for c1 in lines_in_file: 91 | test_plt.append(c1.split()[0]) 92 | train_plt.append(c1.split()[1]) 93 | percent_plt.append(c1.split()[2]) 94 | fig = graph.figure() 95 | ax = fig.add_subplot(111) 96 | graph.plot(percent_plt, test_plt, 'bo', label='Training Error') 97 | graph.plot(percent_plt, train_plt, 'ro', label='Testing Error') 98 | graph.plot(percent_plt, test_plt, 'b') 99 | graph.plot(percent_plt, train_plt, 'r') 100 | ax.set_xlabel('Percentage of Taining data') 101 | ax.set_ylabel('Percentage of Error') 102 | graph.legend( loc='upper left', numpoints = 1 ) 103 | graph.title("% Error Vs % training Data") 104 | graph.show() 105 | return; 106 | 107 | # Starting of the flow of program 108 | read_data = read_data("plrx.txt"); 109 | read_data_labels = read_labels(read_data); 110 | read_data_features = read_features(read_data); 111 | input_percent = [40, 50, 60, 70, 80, 90] 112 | file_created1 = open('Generated_accuracy_table.dat','w') 113 | file_created2 = open('Generated_error_table.dat','w') 114 | for pri in range(0,len(input_percent)): 115 | x1 = split_data(read_data_features,input_percent[pri]); 116 | x2 = split_data(read_data_labels,input_percent[pri]); 117 | read_data_labels_train = x2[0]; 118 | read_data_features_train = x1[0]; 119 | read_data_labels_test = x2[1]; 120 | read_data_features_test = x1[1]; 121 | model_svc = compute_SVC(read_data_features_train,read_data_labels_train); 122 | #print "train" 123 | accu_percent_train = compute_accuracy(read_data_features_train,read_data_labels_train,model_svc)*100; 124 | #print "test" 125 | accu_percent_test = compute_accuracy(read_data_features_test,read_data_labels_test,model_svc)*100; 126 | train_err = compute_error(read_data_features_train,read_data_labels_train,model_svc); 127 | test_err = compute_error(read_data_features_test,read_data_labels_test,model_svc); 128 | file_created1.write("%f %f %f \n" %(accu_percent_train,accu_percent_test,input_percent[pri])) 129 | file_created2.write("%f %f %f \n" %(train_err,test_err,input_percent[pri])) 130 | conf_mat = compute_confusion_matrix(read_data_features_train,read_data_labels_train,model_svc); 131 | print conf_mat 132 | conf_mat1 = compute_confusion_matrix(read_data_features_test,read_data_labels_test,model_svc); 133 | print conf_mat1 134 | file_created1.close() 135 | file_created2.close() 136 | #conf_mat = compute_confusion_matrix(read_data_features_train,read_data_labels_train,model_svc); 137 | #print conf_mat 138 | #conf_mat1 = compute_confusion_matrix(read_data_features_test,read_data_labels_test,model_svc); 139 | #print conf_mat1 140 | #compute_plot("Generated_accuracy_table.dat"); 141 | #compute_plot("Generated_error_table.dat"); 142 | 143 | -------------------------------------------------------------------------------- /plrx.txt: -------------------------------------------------------------------------------- 1 | -1.7936000e-001 -2.0700000e-001 -2.0971000e-001 -9.7260000e-002 -1.1921000e-001 -1.7322000e-001 -2.8076000e-001 2.2317000e-001 4.1866000e-001 -3.2886000e-002 3.3827000e-003 -3.3425000e-001 1.0000000e+000 2 | -1.4659000e-001 -1.6494000e-001 2.4987000e-001 -7.3985000e-002 4.9494000e-001 -1.5633000e-001 -2.8891000e-001 5.0080000e-001 -4.5553000e-002 5.0759000e-002 -1.7511000e-002 6.6959000e-002 1.0000000e+000 3 | -1.3131000e-001 5.1816000e-001 4.3281000e-001 3.9789000e-001 4.3127000e-002 5.6320000e-001 -1.3246000e-002 -6.2833000e-002 -8.8339000e-001 -4.0303000e-002 8.8057000e-002 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