├── LICENSE ├── README.md ├── _config.yml ├── fisher ├── fisher.cpp └── fisher_all.cpp ├── mat ├── CS_SP.m ├── Dict_build.m ├── Dict_build_all.m ├── OMP.m ├── PlaneGT.mat ├── Sandiego.mat ├── detect.m ├── get_target_index.m ├── ima │ ├── Dual_window.png │ ├── GT.png │ ├── ROC.png │ ├── ROC_K_3.png │ ├── SVM.png │ ├── Sparse.png │ ├── fisher_all.png │ ├── fisher_part.png │ ├── ima.png │ ├── main_map.png │ ├── reb.png │ └── rebuilt.png ├── local │ ├── CS_SP.m │ ├── OMP.m │ ├── PlaneGT.mat │ ├── S_part.mat │ ├── get_dict_b.m │ ├── get_target_index.m │ ├── ima │ │ ├── ROC.png │ │ ├── ROC_all.png │ │ ├── ROC_smooth_not.png │ │ ├── Residual.png │ │ ├── local_k_4d.png │ │ ├── local_smooth.png │ │ ├── local_smooth_new.png │ │ ├── local_smooth_new_roc.png │ │ ├── locald.png │ │ ├── mirror.png │ │ ├── pinjie_svm_fisher.png │ │ ├── smooth_ROC.png │ │ └── theta.png │ ├── local_plot.m │ ├── local_smooth.m │ ├── local_smooth_plot.m │ ├── local_suitable.m │ ├── mirror.m │ ├── plot_residual.m │ └── source.m ├── omp_norm.m ├── rebuild.m └── save │ ├── Dict_build │ ├── Dict_build.mat │ ├── Dict_build_ROC_X.mat │ └── Dict_build_ROC_Y.mat │ ├── Dict_build_all │ ├── Dict_build_all_X.mat │ ├── Dict_build_all_Y.mat │ └── Residual.mat │ ├── Map.txt │ ├── PlaneGT.mat │ ├── ROC │ ├── Dict_build_ROC_X.mat │ ├── Dict_build_ROC_Y.mat │ ├── Dict_build_all_X.mat │ ├── Dict_build_all_Y.mat │ ├── Local_Smooth_X_2.mat │ ├── Local_Smooth_Y_2.mat │ ├── Local__X_2.mat │ ├── Local__Y_2.mat │ ├── Plot_smooth_not.m │ ├── local_X.mat │ ├── local_Y.mat │ ├── local_smooth_X.mat │ ├── local_smooth_Y.mat │ └── plot_ROC.m │ ├── fisher_Map.txt │ ├── fisher_Map_all.txt │ ├── local │ ├── Residual.mat │ ├── local_X.mat │ └── local_Y.mat │ └── local_smooth │ ├── Residual.mat │ ├── local_smooth_X.mat │ └── local_smooth_Y.mat └── svm ├── Map.txt ├── data.txt ├── gt.txt ├── main.cpp ├── out.txt └── trainSample.xml /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Shouping Shan 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | ## Introduction 3 | 4 | Hyperspectral image target detection based on [sparse representation](https://en.wikipedia.org/wiki/Sparse_approximation),an effective method in Pattern Recognition. Target detection 5 | aims to separate the specific target pixel from the various backgrounds by the use of known target 6 | pixels or anomalous properties. 7 | The proposed approach relies on the binary hypothesis model of an unknown sample induced by sparse representation. 8 | The sample can be sparsely represented by training samples from the background and target dictionary. The sparse vector 9 | in the model can be recovered by a greedy algorithm [OMP](https://en.wikipedia.org/wiki/Matching_pursuit) . 10 | 11 | ## Author 12 | * [@ShoupingShan](https://github.com/ShoupingShan) 13 | 14 | ## Bibliography 15 | 16 | 1. **[Sub-space Matching](http://ieeexplore.ieee.org/abstract/document/5766028/)** 17 | 18 | *Chen Y, Nasrabadi N M, Tran T D. Hyperspectral image classification using dictionary-based sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3973-3985.* 19 | 2. **[Dual Window](http://ieeexplore.ieee.org/abstract/document/5711635/)** 20 | 21 | *Chen Y, Nasrabadi N M, Tran T D. Sparse representation for target detection in hyperspectral imagery[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(3): 629-640.* 22 | 23 | 24 | ## Theory (SMSD) 25 | 26 | The problem of target detection can be regarded as a competitive relationship of two hypotheses ![](http://latex.codecogs.com/gif.latex?%24H_0%24)(Background) and ![](http://latex.codecogs.com/gif.latex?%24H_1%24)(Target). 27 | 28 | ![](http://latex.codecogs.com/gif.latex?%24%24%5Cbegin%7Baligned%7DH_0%20%26%20%3DB%5Calpha_b+n%20%5C%5C%5C%5CH_1%26%3DT%5Calpha_t+B%5Calpha_b+n%5Cend%7Baligned%7D%20%24%24) 29 | 30 | T and B are both matrices, their column vectors are divided into target and background subspace. ![](http://latex.codecogs.com/gif.latex?%24%5Calpha_t%24) and ![](http://latex.codecogs.com/gif.latex?%24%5Calpha_b%24) form the coefficient vectors of the coefficients, respectively. N denotes Gaussian random noise, [T,B]represents a cascade matrix of T and B. 31 | 32 | ![](http://latex.codecogs.com/gif.latex?%24%24%5Cbegin%7Baligned%7Dn_0%20%26%20%3Dx-B%5Calpha_b%3D%28I-P_B%29x%20%5C%5Cn_1%26%3Dx-T%5Calpha_t+B%5Calpha_b%3D%28I-P_%7BTB%7D%29x%5Cend%7Baligned%7D%20%24%24) 33 | 34 | Suppose ![](http://latex.codecogs.com/gif.latex?D_%7BMSD%7D%28x%29%3D%5Cfrac%7Bx%5ET%281-P_B%20%29x%7D%7Bx%5ET%281-P_%7BTB%7D%29x%7D),When D is greater than a certain threshold η, then X is the target. 35 | 36 | That means we need to find a projection matrix p. 37 | 38 | By the sparse representation of knowledge, it is known that the residual error of signal reconstruction can be expressed as: 39 | 40 | ![](http://latex.codecogs.com/gif.latex?%24%24%5Cbegin%7Baligned%7Dn_0%27%20%26%20%3Dx-A_b%5Calpha%5C%5Cn_1%27%26%3Dx-A%5Cgamma%5Cend%7Baligned%7D%20%24%24) 41 | 42 | After comparison we can find: 43 | 44 | ![](http://latex.codecogs.com/gif.latex?%24%24%5Cbegin%7Baligned%7Dn_0%20%26%20%3D%28I-P_B%29x%5Cto%20x-A_b%5Calpha%27%20%5C%5Cn_1%26%3D%281-P_%7BTB%7D%20%29x%5Cto%20x-A%5Cgamma%5Cend%7Baligned%7D%20%24%24) 45 | 46 | Suppose ![](http://latex.codecogs.com/gif.latex?D_%7BMSD%7D%28x%29%3D%5Cfrac%7Bx%5ET%281-A_b%5Calpha%27%20%29x%7D%7Bx%5ET%281-A%5Cgamma%29x%7D),When D is greater than a certain threshold η, then X is the target. 47 | 48 | Then it is based on the ROC curve to compare different threshold effects, resulting in the final result. 49 | 50 | However, it is better to amend Denominator as ![](http://latex.codecogs.com/gif.latex?%24x%5ET%20%28x-A_t%20%5Cgamma%29x%24) in practice. 51 | 52 | 53 | ## Data 54 | 55 | ### San Diego hyperspectral dataset (400*400) 56 | ![SOURCE](./mat/ima/main_map.png) 57 | 58 | ### GroundTruth (100*100) 59 | ![DT](./mat/ima/GT.png) 60 | 61 | ## File 62 | * `./Dict_build.m` dictionary build with part of target index 63 | * `./Dict_build_all.m` dictionary build with all of the target index 64 | * `detect.m` Basic algorithm to detect target 65 | * `./local/local_suitable.m` Dual_window method with smooth and suitable to adjust and plot 66 | * `./local/local_smooth.m` Dual_window method with smooth 67 | * `./local/local_smooth.m` Dual_window method with smooth and plot results 68 | 69 | ## Rebuilt 70 | 71 | ### Sparse coefficients 72 | ![SC](./mat/local/ima/theta.png) 73 | ### Rebuild by dict_b and dict_t 74 | ![SC_REBUILD](./mat/ima/rebuilt.png) 75 | ## Detection 76 | 77 | ### Sparse Representation 78 | ![SR_good](./mat/ima/Sparse.png) 79 | ![SR_bad](./mat/ima/ima.png) 80 | ### Dual window 81 | ![DW](./mat/ima/Dual_window.png) 82 | ### Dual window with smooth 83 | ![DWS](./mat/local/ima/locald.png) 84 | ### SVM 85 | ![SVM](./mat/ima/SVM.png) 86 | 87 | 88 | | |Positive|Negative|Total|Accuracy| 89 | |:-:|:-----:|:------:|:---:|:------:| 90 | |Train|37|6629|6666|| 91 | |Test|57|9943|10000|0.9989| 92 | 93 | ### Fisher 94 | 95 | 1. Using all of data 96 | 97 | ![ALL](./mat/ima/fisher_all.png) 98 | 99 | 100 | 101 | 102 | | |Positive|Negative|Total|Accuracy| 103 | |:-:|:-----:|:------:|:---:|:------:| 104 | |Train|57|9943|10000|| 105 | |Test|57|9943|10000|0.9926| 106 | 107 | 108 | 2. Using part of the data 109 | 110 | ![PART](./mat/ima/fisher_part.png) 111 | 112 | | |Positive|Negative|Total|Accuracy| 113 | |:-:|:-----:|:------:|:---:|:------:| 114 | |Train|37|6629|6666|| 115 | |Test|57|9943|10000|0.985| 116 | 117 | 118 | ## ROC curve 119 | ![ROC](./mat/local/ima/local_smooth_new_roc.png) 120 | ![ROC_ALL](./mat/local/ima/ROC_all.png) 121 | ## Platform 122 | * [Win10](https://www.microsoft.com/zh-cn) 123 | * [VS2013](http://www.iplaysoft.com/vs2013.html) 124 | * [OpenCV 2.4.9](http://opencv.org/) 125 | * [Matlab r2016b](https://www.mathworks.com/) 126 | ## How to run 127 | > For Sparse Representation 128 | 129 | `mat/detect.m` 130 | 131 | `mat/local/local_smooth.m` 132 | 133 | `mat/local/local_smooth_plot.m` 134 | 135 | `mat/local/local_suitable.m` 136 | > For SVM 137 | 138 | `svm/main.cpp` 139 | > For Fisher_part 140 | 141 | `fisher/fisher.cpp` 142 | > For Fisher_all 143 | 144 | `fisher/fisher_all.cpp` 145 | ## Contact Us 146 | *shp395210@outlook.com* 147 | 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information of the sample data is better than that of the sample data. 11 | [S_h,S_w,S_b]=size(S_); 12 | 13 | %Normalized data 14 | S_temp=zeros(100,100,70); 15 | for i=1:100 16 | for j=1:100 17 | for k=1:70 18 | S_temp(i,j,k)=S_temp(i,j,k)+S_(i,j,k); 19 | end; 20 | end; 21 | end; 22 | S_temp=S_temp/70; 23 | max_=max(max(S_temp)); 24 | max_=max_(:); 25 | min_=min(min(S_temp)); 26 | min_=min_(:); 27 | S=zeros(100,100,70); 28 | for i=1:100 29 | for j=1:100 30 | for k=1:70 31 | S(i,j,:)=(S_temp(i,j,:)-min_(k))/(max_(k)-min_(k))*255; 32 | end; 33 | end; 34 | end; 35 | S=floor(S); 36 | 37 | sum=0; 38 | sum_t_ignore=0; %Record ignored target pixels 39 | sum_b_ignore=0; 40 | for i=1:P_h 41 | for j=1:P_w 42 | if P(i,j)==1 43 | sum=sum+1; 44 | temp=rand(1); 45 | if temp<0.667 %Extract 1/3 of the target pixel as a dictionary 46 | P(i,j)=2; 47 | sum_t_ignore=sum_t_ignore+1; 48 | end; 49 | else 50 | temp=rand(1); 51 | if temp<0.667 %Extract 1/3 of the background pixels as a dictionary 52 | P(i,j)=2; %All pixels marked as 2 will not be used as a dictionary 53 | sum_b_ignore=sum_b_ignore+1; 54 | end; 55 | end; 56 | end; 57 | end; 58 | Dict_t=zeros(S_b,sum-sum_t_ignore); %Target dictionary 59 | Dict_b=zeros(S_b,P_h*P_w-sum-sum_b_ignore); %Background dictionary 60 | index_t=1; 61 | index_b=1; 62 | %% 63 | %%%%%%%%%%%%%%%%%%%%%%%%%% 64 | % Construct target and background dictionary % 65 | %%%%%%%%%%%%%%%%%%%%%%%%%% 66 | for i=1:P_h 67 | for j=1:P_w 68 | if P(i,j)==1 69 | Dict_t(:,index_t)=S(i,j,:); 70 | index_t=index_t+1; 71 | elseif P(i,j)==0 72 | Dict_b(:,index_b)=S(i,j,:); 73 | index_b=index_b+1; 74 | end; 75 | end; 76 | end; 77 | Dict=zeros(S_b,P_h*P_w-sum_b_ignore-sum_t_ignore); %Global dictionary 78 | Dict(:,1:sum-sum_t_ignore)=Dict_t(:,:); 79 | Dict(:,sum-sum_t_ignore+1:P_h*P_w-sum_b_ignore-sum_t_ignore)=Dict_b(:,:); %cascade 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | -------------------------------------------------------------------------------- /mat/Dict_build_all.m: -------------------------------------------------------------------------------- 1 | %--------------------------------------------------------------------------------- 2 | % Supervised dictionary construction based on all of the hyperspectral target data 3 | %--------------------------------------------------------------------------------- 4 | %% 5 | clear all 6 | clc 7 | load PlaneGT.mat 8 | load Sandiego.mat 9 | P=PlaneGT; 10 | [P_h,P_w]=size(P); 11 | S_=Sandiego(1:P_h,1:P_w,10:79); %The original image is cropped to 100*100, and the information of the sample data is better than that of the sample data. 12 | [S_h,S_w,S_b]=size(S_); 13 | 14 | %Normalized data 15 | S_temp=zeros(100,100,70); 16 | for i=1:100 17 | for j=1:100 18 | for k=1:70 19 | S_temp(i,j,k)=S_temp(i,j,k)+S_(i,j,k); 20 | end; 21 | end; 22 | end; 23 | S_temp=S_temp/70; 24 | max_=max(max(S_temp)); 25 | max_=max_(:); 26 | min_=min(min(S_temp)); 27 | min_=min_(:); 28 | S=zeros(100,100,70); 29 | for i=1:100 30 | for j=1:100 31 | for k=1:70 32 | S(i,j,:)=(S_temp(i,j,:)-min_(k))/(max_(k)-min_(k))*255; 33 | end; 34 | end; 35 | end; 36 | S=floor(S); 37 | 38 | 39 | sum=0; 40 | for i=1:P_h 41 | for j=1:P_w 42 | if P(i,j)==1 43 | sum=sum+1; 44 | end; 45 | end; 46 | end; 47 | Dict_t=zeros(S_b,sum); %Target dictionary 48 | Dict_b=zeros(S_b,P_h*P_w-sum); %Background dictionary 49 | index_t=1; 50 | index_b=1; 51 | %% 52 | %%%%%%%%%%%%%%%%%%%%%%%%%% 53 | % Construct target and background dictionary % 54 | %%%%%%%%%%%%%%%%%%%%%%%%%% 55 | for i=1:P_h 56 | for j=1:P_w 57 | if P(i,j)==1 58 | Dict_t(:,index_t)=S(i,j,:); 59 | index_t=index_t+1; 60 | else 61 | Dict_b(:,index_b)=S(i,j,:); 62 | index_b=index_b+1; 63 | end; 64 | end; 65 | end; 66 | Dict=zeros(S_b,P_h*P_w); %Global dictionary 67 | Dict(:,1:sum)=Dict_t(:,:); 68 | Dict(:,sum+1:P_h*P_w)=Dict_b(:,:); %cascade 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Collect(P_h*(i-1)+j)>threshold 28 | P_compare(i,j)=1; 29 | end; 30 | end; 31 | end; 32 | sum_TP=0; %True positive 33 | sum_FP=0; %False positive 34 | sum_FN=0; %False negative 35 | sum_TN=0; %True negative 36 | for i=1:P_h 37 | for j=1:P_w 38 | if P_compare(i,j)==1&&PlaneGT(i,j)==1 39 | sum_TP=sum_TP+1; 40 | elseif P_compare(i,j)==0&&PlaneGT(i,j)==1 41 | sum_FN=sum_FN+1; 42 | elseif P_compare(i,j)==0&&PlaneGT(i,j)==0 43 | sum_TN=sum_TN+1; 44 | elseif P_compare(i,j)==1&&PlaneGT(i,j)==0 45 | sum_FP=sum_FP+1; 46 | end; 47 | end; 48 | end; 49 | FPR=sum_FP/(sum_FP+sum_TN); %False positive rate 50 | TPR=sum_TP/(sum_TP+sum_FN); %True positive rate 51 | distence=(FPR)^2+(1-TPR)^2; 52 | if distenceBEST_THRESHOLD 70 | P_compare(i,j)=1; 71 | end; 72 | end; 73 | end; 74 | 75 | %% Plot 76 | figure 77 | subplot(1,2,1); 78 | imshow(PlaneGT),title('Standard image'); 79 | subplot(1,2,2); 80 | imshow(P_compare),title('Test results'); 81 | 82 | 83 | 84 | 85 | 86 | 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-------------------------------------------------------------------------------- /mat/local/mirror.m: -------------------------------------------------------------------------------- 1 | for i=(len+1):(100+len) 2 | for j=(len+1):(100+len) 3 | if i==(len+1)||j==(len+1)||i==(len+100)||j==(len+100) 4 | S_expand(i,j,20)=255; 5 | end; 6 | end; 7 | end; 8 | imshow(uint8(S_expand(:,:,20))) -------------------------------------------------------------------------------- /mat/local/plot_residual.m: -------------------------------------------------------------------------------- 1 | figure; 2 | plot(gamma); 3 | figure; 4 | plot(y_b,'r') 5 | hold on; 6 | plot(y) 7 | hold on; 8 | plot(y_t,'k') 9 | hold on; 10 | ne=y_t+y_b; 11 | plot(ne,'p'); -------------------------------------------------------------------------------- /mat/local/source.m: -------------------------------------------------------------------------------- 1 | %% show main map(400*400) and used map(100*100) 2 | load Sandiego.mat 3 | X=Sandiego; 4 | Y=zeros(400,400); 5 | X=X(:,:,1:199); 6 | for i=1:400 7 | for j=1:400 8 | for k=1:199 9 | Y(i,j)=Y(i,j)+X(i,j,k); 10 | end; 11 | end; 12 | end; 13 | M=Y/199; 14 | max_=max(max(M)); 15 | min_=min(min(M)); 16 | N=(M-min_)/(max_-min_)*255; 17 | figure; 18 | imshow(floor(uint8(N))),title('main ima'); 19 | ima=floor(uint8(N)); 20 | S_part=ima(1:100,1:100); 21 | figure; 22 | imshow(S_part); -------------------------------------------------------------------------------- /mat/omp_norm.m: -------------------------------------------------------------------------------- 1 | function x = omp_norm(b,A,S) 2 | 3 | % Orthogonal Matching Pursuit (OMP) 4 | % 5 | % Input: 6 | % A: dictionary (matrix) 7 | % b: signal 8 | % S: sparsity level 9 | % Output: 10 | % x: coeff vector for sparse representation 11 | 12 | [N,K] = size(A); % N:dim of signal, K:#atoms in dictionary 13 | if (N ~= size(b)) 14 | error('Dimension not matched'); 15 | end 16 | 17 | x = zeros(K,1); % coefficient (output) 18 | r = b; % residual of b 19 | omega = zeros(S,1); % selected support 20 | A_omega = []; % corresponding columns of A 21 | cnt = 0; 22 | while (cnt < S) % choose S atoms 23 | cnt = cnt+1; 24 | x_tmp = zeros(K,1); 25 | inds = setdiff([1:K],omega); % iterate all columns except for the chosen ones 26 | for i = inds 27 | x_tmp(i) = A(:,i)' * r / norm(A(:,i)); % sol of min ||a'x-b|| 28 | end 29 | [~,ichosen] = max(abs(x_tmp)); % choose the maximum 30 | omega(cnt) = ichosen; 31 | A_omega = [A_omega A(:,ichosen)]; 32 | x_ls = A_omega \ b; % Aomega * x_ls = b 33 | r = b - A_omega * x_ls; % update r 34 | end 35 | 36 | for i = 1:S 37 | x(omega(i)) = x_ls(i); %x_sparse(i).value; 38 | end -------------------------------------------------------------------------------- /mat/rebuild.m: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /mat/save/ROC/Plot_smooth_not.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | load Dict_build_all_X.mat 3 | load Dict_build_all_Y.mat 4 | Dict_build_all_X=X; 5 | Dict_build_all_Y=Y; 6 | load Dict_build_ROC_X.mat 7 | load Dict_build_ROC_Y.mat 8 | Dict_build_ROC_X=X; 9 | Dict_build_ROC_Y=Y; 10 | load Local_Smooth_X_2.mat 11 | load Local_Smooth_Y_2.mat 12 | local_smooth_X=X; 13 | local_smooth_Y=Y; 14 | load Local__X_2.mat 15 | load Local__Y_2.mat 16 | local_X=X; 17 | local_Y=Y; 18 | figure; 19 | plot(local_X,local_Y,'k'),xlabel('False alarm rate','FontSize',14),ylabel('Probability of detection','FontSize',14); 20 | hold on; 21 | plot(local_smooth_X,local_smooth_Y,'g'); 22 | hold on; 23 | legend('Dual window without smooth','Dual window with smooth'); -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/ShoupingShan/Hyperspectral-image-target-detection-based-on-sparse-representation/109f59dc6e7e09d9f13dad4f587ddc706683f7a9/mat/save/ROC/local_smooth_X.mat -------------------------------------------------------------------------------- /mat/save/ROC/local_smooth_Y.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShoupingShan/Hyperspectral-image-target-detection-based-on-sparse-representation/109f59dc6e7e09d9f13dad4f587ddc706683f7a9/mat/save/ROC/local_smooth_Y.mat -------------------------------------------------------------------------------- /mat/save/ROC/plot_ROC.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | load Dict_build_all_X.mat 3 | load Dict_build_all_Y.mat 4 | Dict_build_all_X=X; 5 | Dict_build_all_Y=Y; 6 | load Dict_build_ROC_X.mat 7 | load Dict_build_ROC_Y.mat 8 | Dict_build_ROC_X=X; 9 | Dict_build_ROC_Y=Y; 10 | load Local_Smooth_X_2.mat 11 | load Local_Smooth_Y_2.mat 12 | local_smooth_X=X; 13 | local_smooth_Y=Y; 14 | 15 | load Local__X_2.mat 16 | load Local__Y_2.mat 17 | local_X=X; 18 | local_Y=Y; 19 | figure; 20 | plot(Dict_build_ROC_X,Dict_build_ROC_Y,'b'),xlabel('False alarm rate','FontSize',14),ylabel('Probability of detection','FontSize',14); 21 | hold on; 22 | plot(Dict_build_all_X,Dict_build_all_Y,'r'); 23 | hold on; 24 | plot(local_X,local_Y,'k'); 25 | hold on; 26 | plot(local_smooth_X,local_smooth_Y,'g'); 27 | hold on; 28 | legend('SMO using Randn Dict','SMO using All Dict','Dual window without smooth','Dual window with smooth'); -------------------------------------------------------------------------------- /mat/save/fisher_Map.txt: -------------------------------------------------------------------------------- 1 | 2 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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#include"opencv2/imgproc/imgproc.hpp" 5 | #include"opencv2/highgui/highgui.hpp" 6 | #include"opencv2/ml/ml.hpp" 7 | #include 8 | #include 9 | #include 10 | 11 | #define TESTNUM 3334 ///测试样本数 12 | #define TRAINNUM 6666 ///训练样本数 13 | #define TYPENUM 2 ///类别 14 | #define vec_len 70 15 | #define P_h 100 16 | #define P_w 100 17 | #define TARGETNUM 57 ///目标数 18 | 19 | using namespace std; 20 | using namespace cv; 21 | typedef struct { 22 | float img[vec_len]; 23 | float type; 24 | }datastruct; 25 | typedef struct{ 26 | float val[vec_len]; 27 | }HyperData; 28 | HyperData Data[P_h][P_w]; //存放高光谱数据10~79波段 29 | float trainLabels[TRAINNUM]; 30 | float testLabels[TESTNUM]; 31 | datastruct TRAIND[TRAINNUM]; 32 | datastruct TESTD[TESTNUM]; 33 | Mat train[TRAINNUM]; ///训练数据 34 | Mat test[TESTNUM]; ///测试数据 35 | Mat Test_all[10000]; 36 | Mat trainmat; ///训练矩阵 37 | Mat trainLabel; ///训练标签 38 | Mat testLabel; ///测试标签 39 | int labelGT[P_h][P_w], GT[P_h][P_w]; ///存储groundtruth 40 | int allTestlabel[10000]; 41 | Mat mergeRows(Mat A, Mat B) 42 | { 43 | int totalRows; 44 | if (A.cols == B.cols&&A.type() == B.type()) 45 | { 46 | totalRows = A.rows + B.rows; 47 | } 48 | else 49 | { 50 | std::cout << "Error 维数不匹配!" << std::endl; 51 | } 52 | Mat mergedDescriptors(totalRows, A.cols, A.type()); 53 | Mat submat = mergedDescriptors.rowRange(0, A.rows); 54 | A.copyTo(submat); 55 | submat = mergedDescriptors.rowRange(A.rows, totalRows); 56 | B.copyTo(submat); 57 | return mergedDescriptors; 58 | } 59 | 60 | int main() 61 | { 62 | 63 | 64 | cout << CV_VERSION << endl; 65 | ///读取类标 66 | FILE *label = fopen("gt.txt", "r"); 67 | int l = 10, ss = 0; 68 | for (int i = 0; i < P_h; i++) 69 | { 70 | for (int j = 0; j < P_w; j++) 71 | { 72 | fscanf(label, "%d", &labelGT[i][j]); 73 | GT[i][j] = labelGT[i][j]; //备份 74 | } 75 | } 76 | 77 | ///读取全部样本 78 | FILE *fp = fopen("data.txt", "r"); 79 | for (int i = 0; i= 38) 94 | labelGT[i][j] = 2; //正类测试样本 95 | } 96 | else if (labelGT[i][j] == 0) 97 | { 98 | sum_b++; 99 | if (sum_b >= 6630) 100 | labelGT[i][j] = 3; //负类测试样本 101 | } 102 | } 103 | ///构造训练样本 104 | int TRAIND_index = 0; 105 | for (int i = 0; i < P_h; i++) 106 | { 107 | for (int j = 0; j 2 | 3 | 4 | C_SVC 5 | RBF 6 | 5.0625000000000009e-001 7 | 6.2500000000000000e+001 8 | 9.9999999747524271e-007 9 | 2147483647 10 | 70 11 | 70 12 | 2 13 | 14 | 1 15 | 2 16 |
i
17 | 18 | 0 1 19 | 44 20 | 21 | <_> 22 | 2.92699993e-001 2.99699992e-001 3.02599996e-001 3.06699991e-001 23 | 3.09799999e-001 3.13600004e-001 3.15800011e-001 3.15400004e-001 24 | 3.17000002e-001 3.18199992e-001 3.17699999e-001 3.18899989e-001 25 | 3.18199992e-001 3.17400008e-001 3.17499995e-001 3.16300005e-001 26 | 3.17099988e-001 3.17200005e-001 3.13499987e-001 3.14200014e-001 27 | 3.12700003e-001 3.12099993e-001 3.12999994e-001 3.16399992e-001 28 | 3.15699995e-001 3.14700007e-001 3.13800007e-001 3.15899998e-001 29 | 3.12799990e-001 3.08800012e-001 3.08299989e-001 3.07300001e-001 30 | 3.03499997e-001 3.03799987e-001 3.00700009e-001 2.99299985e-001 31 | 2.96799988e-001 2.95300007e-001 2.95500010e-001 2.94499993e-001 32 | 2.94000000e-001 2.91000009e-001 2.87800014e-001 2.86599994e-001 33 | 2.85800010e-001 2.84299999e-001 2.81500012e-001 2.78899997e-001 34 | 2.79599994e-001 2.78699994e-001 2.78400004e-001 2.76400000e-001 35 | 2.78800011e-001 2.82999992e-001 2.78499991e-001 2.76499987e-001 36 | 2.75200009e-001 2.70999998e-001 2.68900007e-001 2.68400013e-001 37 | 2.65700012e-001 2.63799995e-001 2.61700004e-001 2.57699996e-001 38 | 2.56099999e-001 2.55299985e-001 2.53600001e-001 2.50299990e-001 39 | 2.45900005e-001 2.46900007e-001 40 | <_> 41 | 2.90699989e-001 2.99699992e-001 3.02599996e-001 3.08299989e-001 42 | 3.12000006e-001 3.12900007e-001 3.17200005e-001 3.17299992e-001 43 | 3.17600012e-001 3.18899989e-001 3.19999993e-001 3.19900006e-001 44 | 3.20400000e-001 3.19700003e-001 3.19099993e-001 3.19000006e-001 45 | 3.17699999e-001 3.17200005e-001 3.16900015e-001 3.16399992e-001 46 | 3.15499991e-001 3.16100001e-001 3.15299988e-001 3.20199996e-001 47 | 3.18899989e-001 3.19599986e-001 3.19200009e-001 3.18800002e-001 48 | 3.16300005e-001 3.13199997e-001 3.11699986e-001 3.10600013e-001 49 | 3.07099998e-001 3.07000011e-001 3.04800004e-001 3.02700013e-001 50 | 2.99899995e-001 2.99899995e-001 3.00399989e-001 2.97800004e-001 51 | 2.96900004e-001 2.93500006e-001 2.91099995e-001 2.90699989e-001 52 | 2.89000005e-001 2.87200004e-001 2.84399986e-001 2.82900006e-001 53 | 2.82000005e-001 2.81699985e-001 2.80900002e-001 2.77799994e-001 54 | 2.80900002e-001 2.81800002e-001 2.79199988e-001 2.76300013e-001 55 | 2.77300000e-001 2.73499995e-001 2.71699995e-001 2.70200014e-001 56 | 2.67699987e-001 2.64899999e-001 2.62800008e-001 2.59799987e-001 57 | 2.58100003e-001 2.55899996e-001 2.54999995e-001 2.52400011e-001 58 | 2.47299999e-001 2.46600002e-001 59 | <_> 60 | 2.80999988e-001 2.89099991e-001 2.92600006e-001 2.94800013e-001 61 | 3.00399989e-001 3.00999999e-001 3.04300010e-001 3.05999994e-001 62 | 3.06499988e-001 3.08699995e-001 3.08699995e-001 3.08299989e-001 63 | 3.09100002e-001 3.09199989e-001 3.09599996e-001 3.07799995e-001 64 | 3.08400005e-001 3.07999998e-001 3.07300001e-001 3.05099994e-001 65 | 3.07599992e-001 3.05900007e-001 3.04899991e-001 3.10000002e-001 66 | 3.08899999e-001 3.08899999e-001 3.08800012e-001 3.08299989e-001 67 | 3.06600004e-001 3.03499997e-001 3.02399993e-001 3.00900012e-001 68 | 2.98299998e-001 2.96999991e-001 2.95300007e-001 2.93900013e-001 69 | 2.90600002e-001 2.91500002e-001 2.91099995e-001 2.90100008e-001 70 | 2.89499998e-001 2.86900014e-001 2.83499986e-001 2.83399999e-001 71 | 2.80499995e-001 2.78299987e-001 2.76499987e-001 2.74300009e-001 72 | 2.75799990e-001 2.73699999e-001 2.73499995e-001 2.70999998e-001 73 | 2.77000010e-001 2.80499995e-001 2.75999993e-001 2.73699999e-001 74 | 2.72199988e-001 2.68599987e-001 2.65500009e-001 2.65399992e-001 75 | 2.62600005e-001 2.60699987e-001 2.61299998e-001 2.55800009e-001 76 | 2.54099995e-001 2.52200007e-001 2.50400007e-001 2.47799993e-001 77 | 2.43799999e-001 2.43300006e-001 78 | <_> 79 | 2.03999996e-001 2.05500007e-001 2.09800005e-001 2.11899996e-001 80 | 2.11500004e-001 2.13200003e-001 2.12899998e-001 2.17299998e-001 81 | 2.15100005e-001 2.13400006e-001 2.12799996e-001 2.11999997e-001 82 | 2.09700003e-001 2.07000002e-001 2.06799999e-001 2.06599995e-001 83 | 2.05200002e-001 2.04300001e-001 2.06300005e-001 2.05699995e-001 84 | 2.04899997e-001 2.06100002e-001 2.06400007e-001 2.09900007e-001 85 | 2.09399998e-001 2.08800003e-001 2.09800005e-001 2.08100006e-001 86 | 2.07100004e-001 2.04999998e-001 2.04799995e-001 2.05400005e-001 87 | 2.02900007e-001 2.02500001e-001 1.99499995e-001 2.00399995e-001 88 | 1.99200004e-001 1.97999999e-001 1.97799996e-001 1.97600007e-001 89 | 1.94999993e-001 1.93700001e-001 1.91300005e-001 1.90400004e-001 90 | 1.89999998e-001 1.89199999e-001 1.86900005e-001 1.84599996e-001 91 | 1.83899999e-001 1.83599994e-001 1.82999998e-001 1.82600006e-001 92 | 1.81400001e-001 1.77000001e-001 1.80399999e-001 1.77399993e-001 93 | 1.77200004e-001 1.76400006e-001 1.77000001e-001 1.75600007e-001 94 | 1.74899995e-001 1.73600003e-001 1.64600000e-001 1.71100006e-001 95 | 1.70699999e-001 1.69300005e-001 1.65900007e-001 1.66600004e-001 96 | 1.64000005e-001 1.63900003e-001 97 | <_> 98 | 2.03999996e-001 2.05500007e-001 2.09800005e-001 2.11899996e-001 99 | 2.11500004e-001 2.13200003e-001 2.12899998e-001 2.17299998e-001 100 | 2.15100005e-001 2.13400006e-001 2.12799996e-001 2.11999997e-001 101 | 2.09700003e-001 2.07000002e-001 2.06799999e-001 2.06599995e-001 102 | 2.05200002e-001 2.04300001e-001 2.06300005e-001 2.05699995e-001 103 | 2.04899997e-001 2.06100002e-001 2.06400007e-001 2.09900007e-001 104 | 2.09399998e-001 2.08800003e-001 2.09800005e-001 2.08100006e-001 105 | 2.07100004e-001 2.04999998e-001 2.04799995e-001 2.05400005e-001 106 | 2.02900007e-001 2.02500001e-001 1.99499995e-001 2.00399995e-001 107 | 1.99200004e-001 1.97999999e-001 1.97799996e-001 1.97600007e-001 108 | 1.94999993e-001 1.93700001e-001 1.91300005e-001 1.90400004e-001 109 | 1.89999998e-001 1.89199999e-001 1.86900005e-001 1.84599996e-001 110 | 1.83899999e-001 1.83599994e-001 1.82999998e-001 1.82600006e-001 111 | 1.81400001e-001 1.77000001e-001 1.80399999e-001 1.77399993e-001 112 | 1.77200004e-001 1.76400006e-001 1.77000001e-001 1.75600007e-001 113 | 1.74899995e-001 1.73600003e-001 1.64600000e-001 1.71100006e-001 114 | 1.70699999e-001 1.69300005e-001 1.65900007e-001 1.66600004e-001 115 | 1.64000005e-001 1.63900003e-001 116 | <_> 117 | 5.07399976e-001 5.27599990e-001 5.45499980e-001 5.63899994e-001 118 | 5.72000027e-001 5.89699984e-001 5.99399984e-001 6.15700006e-001 119 | 6.25899971e-001 6.33400023e-001 6.43999994e-001 6.51600003e-001 120 | 6.56300008e-001 6.60300016e-001 6.63500011e-001 6.66499972e-001 121 | 6.70099974e-001 6.73699975e-001 6.74199998e-001 6.78799987e-001 122 | 6.80499971e-001 6.87300026e-001 6.91699982e-001 6.96399987e-001 123 | 7.00699985e-001 7.05999970e-001 7.08400011e-001 7.11399972e-001 124 | 7.10900009e-001 7.13599980e-001 7.06799984e-001 7.11700022e-001 125 | 7.04599977e-001 7.04100013e-001 7.03000009e-001 7.00500011e-001 126 | 6.97600007e-001 6.95999980e-001 6.95999980e-001 6.99000001e-001 127 | 6.93000019e-001 6.93499982e-001 6.91600025e-001 6.87300026e-001 128 | 6.82299972e-001 6.80100024e-001 6.80000007e-001 6.74300015e-001 129 | 6.75700009e-001 6.72299981e-001 6.69000030e-001 6.71000004e-001 130 | 6.67999983e-001 6.71999991e-001 6.68399990e-001 6.65300012e-001 131 | 6.65199995e-001 6.61599994e-001 6.60700023e-001 6.58399999e-001 132 | 6.55600011e-001 6.50500000e-001 6.29299998e-001 6.42400026e-001 133 | 6.39999986e-001 6.34899974e-001 6.30100012e-001 6.25999987e-001 134 | 6.17900014e-001 6.17500007e-001 135 | <_> 136 | 3.67100000e-001 3.81900012e-001 3.84900004e-001 3.90100002e-001 137 | 4.06500012e-001 4.07200009e-001 4.26099986e-001 4.20100003e-001 138 | 4.26099986e-001 4.40299988e-001 4.43300009e-001 4.43800002e-001 139 | 4.56499994e-001 4.60799992e-001 4.62900013e-001 4.62399989e-001 140 | 4.64700013e-001 4.70200002e-001 4.64100003e-001 4.64399993e-001 141 | 4.72999990e-001 4.70600009e-001 4.71500009e-001 4.76500005e-001 142 | 4.75400001e-001 4.76399988e-001 4.79600012e-001 4.81400013e-001 143 | 4.80500013e-001 4.78799999e-001 4.76099998e-001 4.77699995e-001 144 | 4.75899994e-001 4.75899994e-001 4.75800008e-001 4.75300014e-001 145 | 4.70699996e-001 4.71799999e-001 4.72700000e-001 4.70899999e-001 146 | 4.70899999e-001 4.67400014e-001 4.66300011e-001 4.64599997e-001 147 | 4.63099986e-001 4.63400006e-001 4.60799992e-001 4.57399994e-001 148 | 4.60000008e-001 4.57100004e-001 4.58400011e-001 4.57700014e-001 149 | 4.69700009e-001 4.81400013e-001 4.70099986e-001 4.70999986e-001 150 | 4.72799987e-001 4.62099999e-001 4.59600002e-001 4.63200003e-001 151 | 4.57199991e-001 4.52899992e-001 4.94199991e-001 4.44900006e-001 152 | 4.44099993e-001 4.42799985e-001 4.48199987e-001 4.39500004e-001 153 | 4.33200002e-001 4.33400005e-001 154 | <_> 155 | 2.04899997e-001 2.09999993e-001 2.07499996e-001 2.04899997e-001 156 | 2.11400002e-001 2.09099993e-001 2.14300007e-001 2.06799999e-001 157 | 2.06499994e-001 2.11700007e-001 2.08399996e-001 2.04300001e-001 158 | 2.09299996e-001 2.10600004e-001 2.08199993e-001 2.07699999e-001 159 | 2.06300005e-001 2.06000000e-001 2.03999996e-001 2.00000003e-001 160 | 2.04200000e-001 1.99300006e-001 1.95999995e-001 2.01600000e-001 161 | 1.96999997e-001 1.95899993e-001 1.96999997e-001 1.96899995e-001 162 | 1.92300007e-001 1.89700007e-001 1.88500002e-001 1.87500000e-001 163 | 1.84599996e-001 1.84799999e-001 1.84300005e-001 1.80500001e-001 164 | 1.77900001e-001 1.77000001e-001 1.77800000e-001 1.74400002e-001 165 | 1.75300002e-001 1.71000004e-001 1.67999998e-001 1.67799994e-001 166 | 1.67099997e-001 1.64399996e-001 1.61899999e-001 1.61100000e-001 167 | 1.61300004e-001 1.59400001e-001 1.59899995e-001 1.57900006e-001 168 | 1.65700004e-001 1.74300000e-001 1.66299999e-001 1.63100004e-001 169 | 1.62599996e-001 1.55699998e-001 1.53999999e-001 1.54499993e-001 170 | 1.52099997e-001 1.49900004e-001 1.65700004e-001 1.47300005e-001 171 | 1.46699995e-001 1.47300005e-001 1.50600001e-001 1.48100004e-001 172 | 1.47699997e-001 1.48699999e-001 173 | <_> 174 | 1.86600000e-001 1.91400006e-001 1.87800005e-001 1.90899998e-001 175 | 1.95500001e-001 1.92900002e-001 1.98599994e-001 1.92499995e-001 176 | 1.91699997e-001 1.96600005e-001 1.93599999e-001 1.89300001e-001 177 | 1.93700001e-001 1.95299998e-001 1.95800006e-001 1.93800002e-001 178 | 1.92800000e-001 1.93299994e-001 1.91400006e-001 1.91000000e-001 179 | 1.93900004e-001 1.86800003e-001 1.83899999e-001 1.86900005e-001 180 | 1.84599996e-001 1.83400005e-001 1.81799993e-001 1.82099998e-001 181 | 1.79499999e-001 1.77200004e-001 1.77499995e-001 1.76699996e-001 182 | 1.74500003e-001 1.76200002e-001 1.76100001e-001 1.71900004e-001 183 | 1.71399996e-001 1.71000004e-001 1.72999993e-001 1.70599997e-001 184 | 1.71299994e-001 1.68300003e-001 1.66600004e-001 1.66800007e-001 185 | 1.66099995e-001 1.65999994e-001 1.64199993e-001 1.63000003e-001 186 | 1.64600000e-001 1.62499994e-001 1.63499996e-001 1.61899999e-001 187 | 1.70300007e-001 1.77900001e-001 1.68899998e-001 1.68400005e-001 188 | 1.69000000e-001 1.62300006e-001 1.62200004e-001 1.62499994e-001 189 | 1.58999994e-001 1.58000007e-001 1.80099994e-001 1.55300006e-001 190 | 1.55100003e-001 1.53999999e-001 1.55499995e-001 1.52700007e-001 191 | 1.50299996e-001 1.49900004e-001 192 | <_> 193 | 1.77699998e-001 1.81600004e-001 1.78000003e-001 1.77499995e-001 194 | 1.81600004e-001 1.77599996e-001 1.82899997e-001 1.73299998e-001 195 | 1.72000006e-001 1.76300004e-001 1.73299998e-001 1.67699993e-001 196 | 1.69900000e-001 1.70499995e-001 1.70300007e-001 1.68599993e-001 197 | 1.68300003e-001 1.67999998e-001 1.66099995e-001 1.65700004e-001 198 | 1.68899998e-001 1.63100004e-001 1.58899993e-001 1.63900003e-001 199 | 1.59400001e-001 1.59999996e-001 1.59099996e-001 1.59400001e-001 200 | 1.57000005e-001 1.55399993e-001 1.56000003e-001 1.55599996e-001 201 | 1.53200001e-001 1.53999999e-001 1.54100001e-001 1.50999993e-001 202 | 1.49700001e-001 1.49399996e-001 1.51600003e-001 1.49499997e-001 203 | 1.51099995e-001 1.47100002e-001 1.44700006e-001 1.46200001e-001 204 | 1.46400005e-001 1.45400003e-001 1.43600002e-001 1.43000007e-001 205 | 1.43700004e-001 1.43099993e-001 1.44899994e-001 1.42700002e-001 206 | 1.50299996e-001 1.57700002e-001 1.51700005e-001 1.48499995e-001 207 | 1.48699999e-001 1.42000005e-001 1.40799999e-001 1.42900005e-001 208 | 1.40699998e-001 1.38999999e-001 1.50800005e-001 1.36299998e-001 209 | 1.34700000e-001 1.35700002e-001 1.36000007e-001 1.34100005e-001 210 | 1.31500006e-001 1.32200003e-001 211 | <_> 212 | 3.47099990e-001 3.55699986e-001 3.63099992e-001 3.68400007e-001 213 | 3.73899996e-001 3.77299994e-001 3.84000003e-001 3.84299994e-001 214 | 3.89499992e-001 3.95599991e-001 3.93599987e-001 3.96299988e-001 215 | 3.99699986e-001 4.02500004e-001 4.00299996e-001 4.02599990e-001 216 | 4.04599994e-001 4.04799998e-001 4.03600007e-001 4.03200001e-001 217 | 4.07799989e-001 4.07400012e-001 4.08600003e-001 4.13800001e-001 218 | 4.12999988e-001 4.15499985e-001 4.15199995e-001 4.15399998e-001 219 | 4.13399994e-001 4.10600007e-001 4.05999988e-001 4.07099992e-001 220 | 4.04399991e-001 4.04100001e-001 4.00999993e-001 3.97700012e-001 221 | 3.92600000e-001 3.90100002e-001 3.89899999e-001 3.87600005e-001 222 | 3.85899991e-001 3.82900000e-001 3.80899996e-001 3.80800009e-001 223 | 3.81599993e-001 3.82999986e-001 3.84900004e-001 3.86900008e-001 224 | 3.90199989e-001 3.94400001e-001 3.95999998e-001 4.00400013e-001 225 | 4.07900006e-001 4.22399998e-001 4.21400011e-001 4.24499989e-001 226 | 4.27800000e-001 4.25999999e-001 4.26400006e-001 4.30400014e-001 227 | 4.28700000e-001 4.28499997e-001 4.42600012e-001 4.26099986e-001 228 | 4.27100003e-001 4.26400006e-001 4.29600000e-001 4.26099986e-001 229 | 4.20599997e-001 4.21700001e-001 230 | <_> 231 | 2.85800010e-001 2.89900005e-001 2.90199995e-001 2.94200003e-001 232 | 2.94600010e-001 2.94000000e-001 2.94800013e-001 2.96900004e-001 233 | 2.93799996e-001 2.90800005e-001 2.90100008e-001 2.87999988e-001 234 | 2.84099996e-001 2.83199996e-001 2.82400012e-001 2.80499995e-001 235 | 2.79599994e-001 2.79300004e-001 2.79199988e-001 2.78100014e-001 236 | 2.77999997e-001 2.77700007e-001 2.78299987e-001 2.82299995e-001 237 | 2.82799989e-001 2.79599994e-001 2.79700011e-001 2.77999997e-001 238 | 2.77200013e-001 2.74199992e-001 2.71800011e-001 2.70900011e-001 239 | 2.70500004e-001 2.68400013e-001 2.66000003e-001 2.65199989e-001 240 | 2.61900008e-001 2.61500001e-001 2.61000007e-001 2.60600001e-001 241 | 2.57299989e-001 2.54500002e-001 2.51500010e-001 2.50800014e-001 242 | 2.49599993e-001 2.47099996e-001 2.45000005e-001 2.42100000e-001 243 | 2.42200002e-001 2.41500005e-001 2.39999995e-001 2.38199994e-001 244 | 2.40899995e-001 2.39099994e-001 2.37299994e-001 2.34699994e-001 245 | 2.33899996e-001 2.32500002e-001 2.31199995e-001 2.29599997e-001 246 | 2.28799999e-001 2.26400003e-001 2.22599998e-001 2.23600000e-001 247 | 2.21900001e-001 2.19999999e-001 2.16000006e-001 2.15100005e-001 248 | 2.12099999e-001 2.12099999e-001 249 | <_> 250 | 2.89799988e-001 2.95399994e-001 2.98500001e-001 3.03600013e-001 251 | 3.06400001e-001 3.05200011e-001 3.09199989e-001 3.09899986e-001 252 | 3.08600008e-001 3.10000002e-001 3.07099998e-001 3.03499997e-001 253 | 3.06300014e-001 3.05799991e-001 3.05599988e-001 3.03600013e-001 254 | 3.04100007e-001 3.05799991e-001 3.03400010e-001 3.02899987e-001 255 | 3.05299997e-001 3.01999986e-001 2.99800009e-001 3.05999994e-001 256 | 3.03400010e-001 3.01999986e-001 3.00500005e-001 3.00799996e-001 257 | 2.97899991e-001 2.93000013e-001 2.93000013e-001 2.92400002e-001 258 | 2.91099995e-001 2.90600002e-001 2.90600002e-001 2.86300004e-001 259 | 2.84200013e-001 2.83399999e-001 2.84500003e-001 2.80400008e-001 260 | 2.81500012e-001 2.77099997e-001 2.74300009e-001 2.73200005e-001 261 | 2.72000015e-001 2.71299988e-001 2.69199997e-001 2.66600013e-001 262 | 2.67500013e-001 2.64200002e-001 2.67199993e-001 2.64099985e-001 263 | 2.74399996e-001 2.78899997e-001 2.73699999e-001 2.68999994e-001 264 | 2.69600004e-001 2.62800008e-001 2.62100011e-001 2.61799991e-001 265 | 2.57699996e-001 2.55899996e-001 2.77500004e-001 2.50699997e-001 266 | 2.48799995e-001 2.48500004e-001 2.50600010e-001 2.46000007e-001 267 | 2.41999999e-001 2.41500005e-001 268 | <_> 269 | 2.99400002e-001 3.05900007e-001 3.08200002e-001 3.09799999e-001 270 | 3.13499987e-001 3.10799986e-001 3.13899994e-001 3.09799999e-001 271 | 3.10299993e-001 3.10000002e-001 3.07099998e-001 3.03499997e-001 272 | 3.05700004e-001 3.05299997e-001 3.02800000e-001 3.01400006e-001 273 | 3.00900012e-001 3.02300006e-001 2.98700005e-001 2.96099991e-001 274 | 3.01299989e-001 2.98000008e-001 2.93399990e-001 3.02100003e-001 275 | 2.98500001e-001 2.97800004e-001 2.94499993e-001 2.95399994e-001 276 | 2.91500002e-001 2.86199987e-001 2.87299991e-001 2.84599990e-001 277 | 2.83600003e-001 2.84299999e-001 2.83100009e-001 2.79700011e-001 278 | 2.76600003e-001 2.75999993e-001 2.76600003e-001 2.73400009e-001 279 | 2.73600012e-001 2.68799990e-001 2.66000003e-001 2.65399992e-001 280 | 2.63399988e-001 2.62800008e-001 2.59700000e-001 2.57099986e-001 281 | 2.57600009e-001 2.56199986e-001 2.56900012e-001 2.54999995e-001 282 | 2.64499992e-001 2.75500000e-001 2.66600013e-001 2.64600009e-001 283 | 2.63099998e-001 2.54700005e-001 2.51300007e-001 2.52400011e-001 284 | 2.48600006e-001 2.44700000e-001 2.73699999e-001 2.39199996e-001 285 | 2.38199994e-001 2.38199994e-001 2.41600007e-001 2.34899998e-001 286 | 2.30299994e-001 2.30800003e-001 287 | <_> 288 | 2.90699989e-001 2.99699992e-001 3.02599996e-001 3.08299989e-001 289 | 3.12000006e-001 3.12900007e-001 3.17200005e-001 3.17299992e-001 290 | 3.17600012e-001 3.18899989e-001 3.19999993e-001 3.19900006e-001 291 | 3.20400000e-001 3.19700003e-001 3.19099993e-001 3.19000006e-001 292 | 3.17699999e-001 3.17200005e-001 3.16900015e-001 3.16399992e-001 293 | 3.15499991e-001 3.16100001e-001 3.15299988e-001 3.20199996e-001 294 | 3.18899989e-001 3.19599986e-001 3.19200009e-001 3.18800002e-001 295 | 3.16300005e-001 3.13199997e-001 3.11699986e-001 3.10600013e-001 296 | 3.07099998e-001 3.07000011e-001 3.04800004e-001 3.02700013e-001 297 | 2.99899995e-001 2.99899995e-001 3.00399989e-001 2.97800004e-001 298 | 2.96900004e-001 2.93500006e-001 2.91099995e-001 2.90699989e-001 299 | 2.89000005e-001 2.87200004e-001 2.84399986e-001 2.82900006e-001 300 | 2.82000005e-001 2.81699985e-001 2.80900002e-001 2.77799994e-001 301 | 2.80900002e-001 2.81800002e-001 2.79199988e-001 2.76300013e-001 302 | 2.77300000e-001 2.73499995e-001 2.71699995e-001 2.70200014e-001 303 | 2.67699987e-001 2.64899999e-001 2.62800008e-001 2.59799987e-001 304 | 2.58100003e-001 2.55899996e-001 2.54999995e-001 2.52400011e-001 305 | 2.47299999e-001 2.46600002e-001 306 | <_> 307 | 2.44800001e-001 2.52600014e-001 2.52499998e-001 2.54200011e-001 308 | 2.57400006e-001 2.58199990e-001 2.62699991e-001 2.60199994e-001 309 | 2.60500014e-001 2.63200015e-001 2.62800008e-001 2.61299998e-001 310 | 2.65199989e-001 2.65100002e-001 2.63999999e-001 2.63399988e-001 311 | 2.64499992e-001 2.64299989e-001 2.60800004e-001 2.60500014e-001 312 | 2.62499988e-001 2.60600001e-001 2.59200007e-001 2.64099985e-001 313 | 2.61500001e-001 2.60199994e-001 2.60100007e-001 2.61400014e-001 314 | 2.58100003e-001 2.55600005e-001 2.52400011e-001 2.51100004e-001 315 | 2.49100000e-001 2.48400003e-001 2.45399997e-001 2.42400005e-001 316 | 2.39199996e-001 2.37700000e-001 2.37900004e-001 2.33899996e-001 317 | 2.32999995e-001 2.28799999e-001 2.24600002e-001 2.24500000e-001 318 | 2.22499996e-001 2.19999999e-001 2.18300000e-001 2.15800002e-001 319 | 2.15000004e-001 2.12899998e-001 2.12099999e-001 2.10899994e-001 320 | 2.15299994e-001 2.19899997e-001 2.14399993e-001 2.13200003e-001 321 | 2.11700007e-001 2.04500005e-001 2.03700006e-001 2.02700004e-001 322 | 1.99200004e-001 1.97600007e-001 2.17500001e-001 1.93200007e-001 323 | 1.93299994e-001 1.93900004e-001 1.98400006e-001 1.95500001e-001 324 | 1.94000006e-001 1.94999993e-001 325 | <_> 326 | 2.92699993e-001 2.99699992e-001 3.02599996e-001 3.06699991e-001 327 | 3.09799999e-001 3.13600004e-001 3.15800011e-001 3.15400004e-001 328 | 3.17000002e-001 3.18199992e-001 3.17699999e-001 3.18899989e-001 329 | 3.18199992e-001 3.17400008e-001 3.17499995e-001 3.16300005e-001 330 | 3.17099988e-001 3.17200005e-001 3.13499987e-001 3.14200014e-001 331 | 3.12700003e-001 3.12099993e-001 3.12999994e-001 3.16399992e-001 332 | 3.15699995e-001 3.14700007e-001 3.13800007e-001 3.15899998e-001 333 | 3.12799990e-001 3.08800012e-001 3.08299989e-001 3.07300001e-001 334 | 3.03499997e-001 3.03799987e-001 3.00700009e-001 2.99299985e-001 335 | 2.96799988e-001 2.95300007e-001 2.95500010e-001 2.94499993e-001 336 | 2.94000000e-001 2.91000009e-001 2.87800014e-001 2.86599994e-001 337 | 2.85800010e-001 2.84299999e-001 2.81500012e-001 2.78899997e-001 338 | 2.79599994e-001 2.78699994e-001 2.78400004e-001 2.76400000e-001 339 | 2.78800011e-001 2.82999992e-001 2.78499991e-001 2.76499987e-001 340 | 2.75200009e-001 2.70999998e-001 2.68900007e-001 2.68400013e-001 341 | 2.65700012e-001 2.63799995e-001 2.61700004e-001 2.57699996e-001 342 | 2.56099999e-001 2.55299985e-001 2.53600001e-001 2.50299990e-001 343 | 2.45900005e-001 2.46900007e-001 344 | <_> 345 | 1.50299996e-001 1.53899997e-001 1.50999993e-001 1.51600003e-001 346 | 1.54599994e-001 1.51600003e-001 1.55599996e-001 1.49700001e-001 347 | 1.49299994e-001 1.52300000e-001 1.50000006e-001 1.44899994e-001 348 | 1.47699997e-001 1.47799999e-001 1.48599997e-001 1.46599993e-001 349 | 1.45500004e-001 1.45999998e-001 1.44299999e-001 1.41800001e-001 350 | 1.46699995e-001 1.40300006e-001 1.36399999e-001 1.40599996e-001 351 | 1.38500005e-001 1.37799993e-001 1.37199998e-001 1.36600003e-001 352 | 1.34399995e-001 1.33100003e-001 1.32200003e-001 1.31400004e-001 353 | 1.29999995e-001 1.31799996e-001 1.29700005e-001 1.28500000e-001 354 | 1.26699999e-001 1.27200007e-001 1.27599999e-001 1.25900000e-001 355 | 1.25799999e-001 1.23499997e-001 1.21900000e-001 1.23199999e-001 356 | 1.22100003e-001 1.21299997e-001 1.19800001e-001 1.18900001e-001 357 | 1.18900001e-001 1.18299998e-001 1.17899999e-001 1.15999997e-001 358 | 1.23700000e-001 1.34900004e-001 1.27200007e-001 1.22599997e-001 359 | 1.23599999e-001 1.17799997e-001 1.15999997e-001 1.17200002e-001 360 | 1.15599997e-001 1.13899998e-001 1.38799995e-001 1.11299999e-001 361 | 1.11400001e-001 1.11500002e-001 1.14600003e-001 1.10100001e-001 362 | 1.08700000e-001 1.08700000e-001 363 | <_> 364 | 2.33300000e-001 2.35599995e-001 2.37000003e-001 2.39399999e-001 365 | 2.41500005e-001 2.39899993e-001 2.43599996e-001 2.40899995e-001 366 | 2.42300004e-001 2.44599998e-001 2.44100004e-001 2.42500007e-001 367 | 2.44000003e-001 2.43699998e-001 2.42500007e-001 2.41500005e-001 368 | 2.41500005e-001 2.40099996e-001 2.40099996e-001 2.40199998e-001 369 | 2.38499999e-001 2.40099996e-001 2.39500001e-001 2.43599996e-001 370 | 2.41400003e-001 2.40999997e-001 2.41200000e-001 2.41799995e-001 371 | 2.39600003e-001 2.37100005e-001 2.37000003e-001 2.36599997e-001 372 | 2.34400004e-001 2.34599993e-001 2.32600003e-001 2.31099993e-001 373 | 2.29599997e-001 2.28699997e-001 2.28699997e-001 2.26899996e-001 374 | 2.26199999e-001 2.23100007e-001 2.21499994e-001 2.20599994e-001 375 | 2.18799993e-001 2.17399999e-001 2.15200007e-001 2.13900000e-001 376 | 2.14499995e-001 2.12300003e-001 2.12899998e-001 2.11400002e-001 377 | 2.13000000e-001 2.08399996e-001 2.13699996e-001 2.11799994e-001 378 | 2.10400000e-001 2.09700003e-001 2.08299994e-001 2.07300007e-001 379 | 2.06100002e-001 2.03899994e-001 1.87299997e-001 2.00800002e-001 380 | 2.00499997e-001 1.98400006e-001 1.96999997e-001 1.95899993e-001 381 | 1.93200007e-001 1.92900002e-001 382 | <_> 383 | 1.93399996e-001 1.95800006e-001 1.99200004e-001 2.05799997e-001 384 | 2.02800006e-001 2.06300005e-001 2.04799995e-001 2.09900007e-001 385 | 2.07800001e-001 2.03199998e-001 2.03799993e-001 2.01600000e-001 386 | 1.97799996e-001 1.97200000e-001 1.95299998e-001 1.94299996e-001 387 | 1.92699999e-001 1.92200005e-001 1.91900000e-001 1.92200005e-001 388 | 1.90500006e-001 1.90300003e-001 1.89099997e-001 1.92499995e-001 389 | 1.91900000e-001 1.89600006e-001 1.88600004e-001 1.88199997e-001 390 | 1.87000006e-001 1.83100000e-001 1.81299999e-001 1.81400001e-001 391 | 1.80500001e-001 1.80199996e-001 1.79600000e-001 1.77699998e-001 392 | 1.76100001e-001 1.75600007e-001 1.75300002e-001 1.73700005e-001 393 | 1.74199998e-001 1.70200005e-001 1.69000000e-001 1.69499993e-001 394 | 1.67799994e-001 1.65800005e-001 1.64700001e-001 1.62699997e-001 395 | 1.63900003e-001 1.63000003e-001 1.62699997e-001 1.60200000e-001 396 | 1.64399996e-001 1.65000007e-001 1.65800005e-001 1.63000003e-001 397 | 1.63100004e-001 1.58999994e-001 1.58099994e-001 1.58000007e-001 398 | 1.56000003e-001 1.54699996e-001 1.58099994e-001 1.51600003e-001 399 | 1.51099995e-001 1.50999993e-001 1.50299996e-001 1.48399994e-001 400 | 1.46100000e-001 1.45699993e-001 401 | <_> 402 | 2.57600009e-001 2.63200015e-001 2.63999999e-001 2.65899986e-001 403 | 2.67500013e-001 2.66499996e-001 2.67500013e-001 2.62699991e-001 404 | 2.62899995e-001 2.64899999e-001 2.63999999e-001 2.60100007e-001 405 | 2.65599996e-001 2.64800012e-001 2.63500005e-001 2.63799995e-001 406 | 2.65500009e-001 2.66499996e-001 2.61900008e-001 2.60699987e-001 407 | 2.65599996e-001 2.64999986e-001 2.64299989e-001 2.69499987e-001 408 | 2.65500009e-001 2.66499996e-001 2.66700000e-001 2.68500000e-001 409 | 2.66200006e-001 2.62600005e-001 2.61700004e-001 2.61799991e-001 410 | 2.60399997e-001 2.61999995e-001 2.59400010e-001 2.59499997e-001 411 | 2.55800009e-001 2.56399989e-001 2.57200003e-001 2.53699988e-001 412 | 2.53500015e-001 2.50299990e-001 2.47999996e-001 2.49899998e-001 413 | 2.49699995e-001 2.47799993e-001 2.46700004e-001 2.44900003e-001 414 | 2.45100006e-001 2.41400003e-001 2.41899997e-001 2.41200000e-001 415 | 2.47700006e-001 2.55199999e-001 2.51800001e-001 2.52299994e-001 416 | 2.54200011e-001 2.48199999e-001 2.46500000e-001 2.47299999e-001 417 | 2.43900001e-001 2.41099998e-001 2.53600001e-001 2.34999999e-001 418 | 2.34200001e-001 2.33700007e-001 2.37499997e-001 2.31299996e-001 419 | 2.27799997e-001 2.26899996e-001 420 | <_> 421 | 2.87800014e-001 2.95199990e-001 3.00099999e-001 3.05099994e-001 422 | 3.09100002e-001 3.10799986e-001 3.15200001e-001 3.15400004e-001 423 | 3.16399992e-001 3.17000002e-001 3.18300009e-001 3.18300009e-001 424 | 3.17600012e-001 3.17900002e-001 3.17400008e-001 3.17400008e-001 425 | 3.17099988e-001 3.16100001e-001 3.14700007e-001 3.14200014e-001 426 | 3.14900011e-001 3.13300014e-001 3.12999994e-001 3.17600012e-001 427 | 3.14900011e-001 3.16599995e-001 3.15299988e-001 3.15800011e-001 428 | 3.13899994e-001 3.10299993e-001 3.07700008e-001 3.08999985e-001 429 | 3.05200011e-001 3.03499997e-001 3.01699996e-001 2.99899995e-001 430 | 2.96799988e-001 2.96000004e-001 2.96299994e-001 2.95800000e-001 431 | 2.94999987e-001 2.91200012e-001 2.89700001e-001 2.88100004e-001 432 | 2.86900014e-001 2.83499986e-001 2.81800002e-001 2.80400008e-001 433 | 2.80400008e-001 2.76699990e-001 2.75700003e-001 2.75599986e-001 434 | 2.79300004e-001 2.81199992e-001 2.78400004e-001 2.75099993e-001 435 | 2.74599999e-001 2.70000011e-001 2.69199997e-001 2.68200010e-001 436 | 2.65300006e-001 2.62899995e-001 2.60500014e-001 2.58300006e-001 437 | 2.56700009e-001 2.54400015e-001 2.52700001e-001 2.50000000e-001 438 | 2.46099994e-001 2.44800001e-001 439 | <_> 440 | 2.87800014e-001 2.96099991e-001 2.98400015e-001 3.04399997e-001 441 | 3.07599992e-001 3.11500013e-001 3.13800007e-001 3.14799994e-001 442 | 3.15699995e-001 3.17699999e-001 3.17099988e-001 3.18300009e-001 443 | 3.18199992e-001 3.17900002e-001 3.17400008e-001 3.17400008e-001 444 | 3.17699999e-001 3.16599995e-001 3.15200001e-001 3.13600004e-001 445 | 3.14300001e-001 3.13800007e-001 3.14099997e-001 3.18199992e-001 446 | 3.17900002e-001 3.15800011e-001 3.15899998e-001 3.16900015e-001 447 | 3.13600004e-001 3.10000002e-001 3.07999998e-001 3.07500005e-001 448 | 3.04899991e-001 3.03000003e-001 3.02199990e-001 3.01400006e-001 449 | 2.96400011e-001 2.97399998e-001 2.96999991e-001 2.95800000e-001 450 | 2.94600010e-001 2.92100012e-001 2.88899988e-001 2.87800014e-001 451 | 2.86900014e-001 2.84299999e-001 2.82400012e-001 2.80499995e-001 452 | 2.80000001e-001 2.77900010e-001 2.77599990e-001 2.75700003e-001 453 | 2.80900002e-001 2.77200013e-001 2.78699994e-001 2.76100010e-001 454 | 2.73900002e-001 2.70399988e-001 2.69600004e-001 2.68200010e-001 455 | 2.66700000e-001 2.63000011e-001 2.61299998e-001 2.58700013e-001 456 | 2.57600009e-001 2.55299985e-001 2.53899992e-001 2.50499994e-001 457 | 2.46299997e-001 2.46199995e-001 458 | <_> 459 | 2.83899993e-001 2.92600006e-001 2.96000004e-001 3.01999986e-001 460 | 3.03999990e-001 3.06600004e-001 3.11699986e-001 3.12900007e-001 461 | 3.15699995e-001 3.15899998e-001 3.17099988e-001 3.17200005e-001 462 | 3.18199992e-001 3.17900002e-001 3.17400008e-001 3.16900015e-001 463 | 3.17099988e-001 3.16599995e-001 3.15200001e-001 3.14700007e-001 464 | 3.15499991e-001 3.14399987e-001 3.15299988e-001 3.18500012e-001 465 | 3.17799985e-001 3.18599999e-001 3.17699999e-001 3.17299992e-001 466 | 3.16700011e-001 3.12299997e-001 3.11600000e-001 3.10499996e-001 467 | 3.07900012e-001 3.06600004e-001 3.05400014e-001 3.03200006e-001 468 | 3.00599992e-001 2.99899995e-001 2.99299985e-001 2.98999995e-001 469 | 2.96999991e-001 2.94999987e-001 2.91700006e-001 2.90499985e-001 470 | 2.89600015e-001 2.86700010e-001 2.85100013e-001 2.83499986e-001 471 | 2.83199996e-001 2.81399995e-001 2.80600011e-001 2.77500004e-001 472 | 2.82700002e-001 2.83399999e-001 2.82999992e-001 2.77799994e-001 473 | 2.78100014e-001 2.73900002e-001 2.72500008e-001 2.72199988e-001 474 | 2.69499987e-001 2.66799986e-001 2.68000007e-001 2.61500001e-001 475 | 2.59799987e-001 2.59400010e-001 2.56700009e-001 2.52799988e-001 476 | 2.50600010e-001 2.47700006e-001 477 | <_> 478 | 2.12799996e-001 2.17999995e-001 2.12500006e-001 2.11300001e-001 479 | 2.16600001e-001 2.10500002e-001 2.16299996e-001 2.04999998e-001 480 | 2.04699993e-001 2.09199995e-001 2.04400003e-001 1.99200004e-001 481 | 2.01800004e-001 2.02800006e-001 1.99699998e-001 1.99100003e-001 482 | 1.97099999e-001 1.98500007e-001 1.94900006e-001 1.94399998e-001 483 | 1.97799996e-001 1.93100005e-001 1.87999994e-001 1.94000006e-001 484 | 1.91100001e-001 1.89099997e-001 1.88500002e-001 1.88199997e-001 485 | 1.85800001e-001 1.83100000e-001 1.81299999e-001 1.80600002e-001 486 | 1.80199996e-001 1.81199998e-001 1.82699993e-001 1.78100005e-001 487 | 1.75799996e-001 1.75300002e-001 1.78000003e-001 1.74600005e-001 488 | 1.75699994e-001 1.71800002e-001 1.70399994e-001 1.71299994e-001 489 | 1.70300007e-001 1.69499993e-001 1.67199999e-001 1.66600004e-001 490 | 1.66700006e-001 1.64100006e-001 1.66099995e-001 1.62799999e-001 491 | 1.71200007e-001 1.76300004e-001 1.70300007e-001 1.69599995e-001 492 | 1.70399994e-001 1.65399998e-001 1.63699999e-001 1.64299995e-001 493 | 1.61200002e-001 1.58600003e-001 1.76899999e-001 1.56499997e-001 494 | 1.55399993e-001 1.54899999e-001 1.56399995e-001 1.52899995e-001 495 | 1.49700001e-001 1.48900002e-001 496 | <_> 497 | 2.02900007e-001 2.03799993e-001 2.08199993e-001 2.13499993e-001 498 | 2.12099999e-001 2.18999997e-001 2.16299996e-001 2.22900003e-001 499 | 2.21799999e-001 2.19999999e-001 2.20300004e-001 2.19200000e-001 500 | 2.15499997e-001 2.16900006e-001 2.15200007e-001 2.14599997e-001 501 | 2.13900000e-001 2.15200007e-001 2.14300007e-001 2.13599995e-001 502 | 2.13400006e-001 2.12899998e-001 2.12799996e-001 2.15200007e-001 503 | 2.13799998e-001 2.13400006e-001 2.12200001e-001 2.12099999e-001 504 | 2.09999993e-001 2.07900003e-001 2.05799997e-001 2.06300005e-001 505 | 2.04999998e-001 2.05699995e-001 2.03999996e-001 2.01800004e-001 506 | 2.00499997e-001 2.00200006e-001 1.99499995e-001 1.98699996e-001 507 | 1.97300002e-001 1.95800006e-001 1.93000004e-001 1.93100005e-001 508 | 1.91699997e-001 1.91000000e-001 1.88600004e-001 1.87600002e-001 509 | 1.86800003e-001 1.86100006e-001 1.85699999e-001 1.84699997e-001 510 | 1.88400000e-001 1.93599999e-001 1.90899998e-001 1.86800003e-001 511 | 1.88500002e-001 1.82699993e-001 1.80700004e-001 1.81999996e-001 512 | 1.79800004e-001 1.77499995e-001 1.92399994e-001 1.74300000e-001 513 | 1.73099995e-001 1.72900006e-001 1.74099997e-001 1.70399994e-001 514 | 1.68099999e-001 1.67600006e-001 515 | <_> 516 | 2.27500007e-001 2.28599995e-001 2.36200005e-001 2.42599994e-001 517 | 2.35599995e-001 2.42100000e-001 2.32700005e-001 2.49699995e-001 518 | 2.43399993e-001 2.33199999e-001 2.34500006e-001 2.38000005e-001 519 | 2.27599993e-001 2.22299993e-001 2.21200004e-001 2.21200004e-001 520 | 2.19400004e-001 2.15900004e-001 2.18999997e-001 2.19999999e-001 521 | 2.14699998e-001 2.19200000e-001 2.21000001e-001 2.23600000e-001 522 | 2.24099994e-001 2.24999994e-001 2.22200006e-001 2.21900001e-001 523 | 2.19699994e-001 2.16000006e-001 2.15499997e-001 2.14300007e-001 524 | 2.13200003e-001 2.10600004e-001 2.08700001e-001 2.07699999e-001 525 | 2.05899999e-001 2.04400003e-001 2.03899994e-001 2.03899994e-001 526 | 2.00900003e-001 1.99399993e-001 1.97899997e-001 1.96400002e-001 527 | 1.94800004e-001 1.91799998e-001 1.90799996e-001 1.87700003e-001 528 | 1.87399998e-001 1.85900003e-001 1.85599998e-001 1.84799999e-001 529 | 1.81799993e-001 1.74700007e-001 1.78100005e-001 1.76100001e-001 530 | 1.74899995e-001 1.75699994e-001 1.76200002e-001 1.72800004e-001 531 | 1.73099995e-001 1.71000004e-001 1.55200005e-001 1.69000000e-001 532 | 1.67500004e-001 1.66299999e-001 1.62400007e-001 1.62000000e-001 533 | 1.60400003e-001 1.60400003e-001 534 | <_> 535 | 2.98400015e-001 3.03200006e-001 3.03299993e-001 3.06600004e-001 536 | 3.07599992e-001 3.06699991e-001 3.07700008e-001 3.04800004e-001 537 | 3.03499997e-001 3.00399989e-001 2.98500001e-001 2.94000000e-001 538 | 2.93199986e-001 2.91999996e-001 2.91000009e-001 2.89000005e-001 539 | 2.88399994e-001 2.88399994e-001 2.87800014e-001 2.85400003e-001 540 | 2.88800001e-001 2.85499990e-001 2.83499986e-001 2.89999992e-001 541 | 2.87999988e-001 2.86599994e-001 2.85800010e-001 2.84399986e-001 542 | 2.82599986e-001 2.77399987e-001 2.76899993e-001 2.77999997e-001 543 | 2.74199992e-001 2.74300009e-001 2.72399992e-001 2.70599991e-001 544 | 2.67300010e-001 2.66099989e-001 2.66499996e-001 2.65500009e-001 545 | 2.64400005e-001 2.59799987e-001 2.56999999e-001 2.56000012e-001 546 | 2.54799992e-001 2.52400011e-001 2.50299990e-001 2.49200001e-001 547 | 2.47500002e-001 2.47999996e-001 2.48500004e-001 2.46700004e-001 548 | 2.54999995e-001 2.58599997e-001 2.53899992e-001 2.49699995e-001 549 | 2.49400005e-001 2.43499994e-001 2.39399999e-001 2.39600003e-001 550 | 2.37200007e-001 2.33600006e-001 2.45499998e-001 2.30599999e-001 551 | 2.27699995e-001 2.25999996e-001 2.27799997e-001 2.24299997e-001 552 | 2.19999999e-001 2.20599994e-001 553 | <_> 554 | 3.09399992e-001 3.13899994e-001 3.16599995e-001 3.16900015e-001 555 | 3.21000010e-001 3.19299996e-001 3.18599999e-001 3.17900002e-001 556 | 3.15899998e-001 3.15400004e-001 3.10499996e-001 3.07300001e-001 557 | 3.07900012e-001 3.05799991e-001 3.03900003e-001 3.01999986e-001 558 | 3.03600013e-001 3.01699996e-001 2.99800009e-001 2.98299998e-001 559 | 3.01800013e-001 3.00300002e-001 2.98000008e-001 3.04699987e-001 560 | 3.02100003e-001 3.01899999e-001 3.00599992e-001 2.99499989e-001 561 | 2.97100008e-001 2.93900013e-001 2.90800005e-001 2.93099999e-001 562 | 2.89099991e-001 2.88700014e-001 2.86700010e-001 2.86300004e-001 563 | 2.82599986e-001 2.81199992e-001 2.81300008e-001 2.78899997e-001 564 | 2.77799994e-001 2.75200009e-001 2.73200005e-001 2.71299988e-001 565 | 2.69699991e-001 2.67800003e-001 2.65799999e-001 2.62300014e-001 566 | 2.63200015e-001 2.62300014e-001 2.60100007e-001 2.58599997e-001 567 | 2.64600009e-001 2.72700012e-001 2.64699996e-001 2.62899995e-001 568 | 2.63200015e-001 2.56900012e-001 2.54900008e-001 2.55600005e-001 569 | 2.51700014e-001 2.48799995e-001 2.63599992e-001 2.43200004e-001 570 | 2.42300004e-001 2.39999995e-001 2.42100000e-001 2.38100007e-001 571 | 2.33700007e-001 2.33400002e-001 572 | <_> 573 | 2.85800010e-001 2.89900005e-001 2.90199995e-001 2.94200003e-001 574 | 2.94600010e-001 2.94000000e-001 2.94800013e-001 2.96900004e-001 575 | 2.93799996e-001 2.90800005e-001 2.90100008e-001 2.87999988e-001 576 | 2.84099996e-001 2.83199996e-001 2.82400012e-001 2.80499995e-001 577 | 2.79599994e-001 2.79300004e-001 2.79199988e-001 2.78100014e-001 578 | 2.77999997e-001 2.77700007e-001 2.78299987e-001 2.82299995e-001 579 | 2.82799989e-001 2.79599994e-001 2.79700011e-001 2.77999997e-001 580 | 2.77200013e-001 2.74199992e-001 2.71800011e-001 2.70900011e-001 581 | 2.70500004e-001 2.68400013e-001 2.66000003e-001 2.65199989e-001 582 | 2.61900008e-001 2.61500001e-001 2.61000007e-001 2.60600001e-001 583 | 2.57299989e-001 2.54500002e-001 2.51500010e-001 2.50800014e-001 584 | 2.49599993e-001 2.47099996e-001 2.45000005e-001 2.42100000e-001 585 | 2.42200002e-001 2.41500005e-001 2.39999995e-001 2.38199994e-001 586 | 2.40899995e-001 2.39099994e-001 2.37299994e-001 2.34699994e-001 587 | 2.33899996e-001 2.32500002e-001 2.31199995e-001 2.29599997e-001 588 | 2.28799999e-001 2.26400003e-001 2.22599998e-001 2.23600000e-001 589 | 2.21900001e-001 2.19999999e-001 2.16000006e-001 2.15100005e-001 590 | 2.12099999e-001 2.12099999e-001 591 | <_> 592 | 2.89799988e-001 2.95399994e-001 2.98500001e-001 3.03600013e-001 593 | 3.06400001e-001 3.05200011e-001 3.09199989e-001 3.09899986e-001 594 | 3.08600008e-001 3.10000002e-001 3.07099998e-001 3.03499997e-001 595 | 3.06300014e-001 3.05799991e-001 3.05599988e-001 3.03600013e-001 596 | 3.04100007e-001 3.05799991e-001 3.03400010e-001 3.02899987e-001 597 | 3.05299997e-001 3.01999986e-001 2.99800009e-001 3.05999994e-001 598 | 3.03400010e-001 3.01999986e-001 3.00500005e-001 3.00799996e-001 599 | 2.97899991e-001 2.93000013e-001 2.93000013e-001 2.92400002e-001 600 | 2.91099995e-001 2.90600002e-001 2.90600002e-001 2.86300004e-001 601 | 2.84200013e-001 2.83399999e-001 2.84500003e-001 2.80400008e-001 602 | 2.81500012e-001 2.77099997e-001 2.74300009e-001 2.73200005e-001 603 | 2.72000015e-001 2.71299988e-001 2.69199997e-001 2.66600013e-001 604 | 2.67500013e-001 2.64200002e-001 2.67199993e-001 2.64099985e-001 605 | 2.74399996e-001 2.78899997e-001 2.73699999e-001 2.68999994e-001 606 | 2.69600004e-001 2.62800008e-001 2.62100011e-001 2.61799991e-001 607 | 2.57699996e-001 2.55899996e-001 2.77500004e-001 2.50699997e-001 608 | 2.48799995e-001 2.48500004e-001 2.50600010e-001 2.46000007e-001 609 | 2.41999999e-001 2.41500005e-001 610 | <_> 611 | 3.54099989e-001 3.60100001e-001 3.63999993e-001 3.65399987e-001 612 | 3.68999988e-001 3.66299987e-001 3.67100000e-001 3.66400003e-001 613 | 3.66299987e-001 3.65099996e-001 3.61999989e-001 3.60500008e-001 614 | 3.58799994e-001 3.56500000e-001 3.54900002e-001 3.53300005e-001 615 | 3.51399988e-001 3.49000007e-001 3.50400001e-001 3.49500000e-001 616 | 3.48300010e-001 3.49700004e-001 3.48399997e-001 3.55199993e-001 617 | 3.53199989e-001 3.52100015e-001 3.51000011e-001 3.50499988e-001 618 | 3.47900003e-001 3.44700009e-001 3.42099994e-001 3.40299994e-001 619 | 3.38200003e-001 3.35799992e-001 3.34399998e-001 3.32100004e-001 620 | 3.28299999e-001 3.27199996e-001 3.25800002e-001 3.24900001e-001 621 | 3.23500007e-001 3.21099997e-001 3.15499991e-001 3.14799994e-001 622 | 3.11199993e-001 3.09100002e-001 3.07399988e-001 3.03999990e-001 623 | 3.05500001e-001 3.03299993e-001 3.03799987e-001 3.01200002e-001 624 | 3.04699987e-001 3.09100002e-001 3.06499988e-001 2.99199998e-001 625 | 2.98299998e-001 2.92299986e-001 2.90899992e-001 2.89200008e-001 626 | 2.86500007e-001 2.82999992e-001 2.71699995e-001 2.79100001e-001 627 | 2.75900006e-001 2.73699999e-001 2.72300005e-001 2.69300014e-001 628 | 2.65500009e-001 2.66200006e-001 629 | <_> 630 | 2.14699998e-001 2.13400006e-001 2.16499999e-001 2.19799995e-001 631 | 2.17999995e-001 2.18799993e-001 2.12899998e-001 2.19799995e-001 632 | 2.16199994e-001 2.09700003e-001 2.08399996e-001 2.07499996e-001 633 | 1.99399993e-001 1.97200000e-001 1.94700003e-001 1.93700001e-001 634 | 1.92100003e-001 1.89199999e-001 1.91400006e-001 1.91599995e-001 635 | 1.88299999e-001 1.87900007e-001 1.88999996e-001 1.90699995e-001 636 | 1.90500006e-001 1.90099999e-001 1.89700007e-001 1.88500002e-001 637 | 1.86299995e-001 1.84200004e-001 1.82200000e-001 1.81099996e-001 638 | 1.80700004e-001 1.79900005e-001 1.76899999e-001 1.75799996e-001 639 | 1.74700007e-001 1.73600003e-001 1.73600003e-001 1.73099995e-001 640 | 1.70300007e-001 1.68099999e-001 1.66700006e-001 1.65600002e-001 641 | 1.64000005e-001 1.63100004e-001 1.60600007e-001 1.59299999e-001 642 | 1.59799993e-001 1.57399997e-001 1.56000003e-001 1.54899999e-001 643 | 1.55399993e-001 1.52400002e-001 1.52400002e-001 1.51800007e-001 644 | 1.50099993e-001 1.49499997e-001 1.49299994e-001 1.48200005e-001 645 | 1.47200003e-001 1.45300001e-001 1.41700000e-001 1.43399999e-001 646 | 1.42499998e-001 1.40799999e-001 1.38899997e-001 1.37300000e-001 647 | 1.36099994e-001 1.35900006e-001 648 | <_> 649 | 2.98400015e-001 3.03200006e-001 3.03299993e-001 3.06600004e-001 650 | 3.07599992e-001 3.06699991e-001 3.07700008e-001 3.04800004e-001 651 | 3.03499997e-001 3.00399989e-001 2.98500001e-001 2.94000000e-001 652 | 2.93199986e-001 2.91999996e-001 2.91000009e-001 2.89000005e-001 653 | 2.88399994e-001 2.88399994e-001 2.87800014e-001 2.85400003e-001 654 | 2.88800001e-001 2.85499990e-001 2.83499986e-001 2.89999992e-001 655 | 2.87999988e-001 2.86599994e-001 2.85800010e-001 2.84399986e-001 656 | 2.82599986e-001 2.77399987e-001 2.76899993e-001 2.77999997e-001 657 | 2.74199992e-001 2.74300009e-001 2.72399992e-001 2.70599991e-001 658 | 2.67300010e-001 2.66099989e-001 2.66499996e-001 2.65500009e-001 659 | 2.64400005e-001 2.59799987e-001 2.56999999e-001 2.56000012e-001 660 | 2.54799992e-001 2.52400011e-001 2.50299990e-001 2.49200001e-001 661 | 2.47500002e-001 2.47999996e-001 2.48500004e-001 2.46700004e-001 662 | 2.54999995e-001 2.58599997e-001 2.53899992e-001 2.49699995e-001 663 | 2.49400005e-001 2.43499994e-001 2.39399999e-001 2.39600003e-001 664 | 2.37200007e-001 2.33600006e-001 2.45499998e-001 2.30599999e-001 665 | 2.27699995e-001 2.25999996e-001 2.27799997e-001 2.24299997e-001 666 | 2.19999999e-001 2.20599994e-001 667 | <_> 668 | 2.62499988e-001 2.66799986e-001 2.67300010e-001 2.66900003e-001 669 | 2.69899994e-001 2.69499987e-001 2.71600008e-001 2.67100006e-001 670 | 2.68000007e-001 2.68599987e-001 2.67399997e-001 2.64699996e-001 671 | 2.64699996e-001 2.65300006e-001 2.62699991e-001 2.61299998e-001 672 | 2.61700004e-001 2.59099990e-001 2.59200007e-001 2.58500010e-001 673 | 2.57600009e-001 2.58399993e-001 2.58599997e-001 2.62499988e-001 674 | 2.60600001e-001 2.59900004e-001 2.58300006e-001 2.59099990e-001 675 | 2.56300002e-001 2.54000008e-001 2.53800005e-001 2.52900004e-001 676 | 2.49699995e-001 2.49500006e-001 2.47600004e-001 2.46999994e-001 677 | 2.43599996e-001 2.42400005e-001 2.43599996e-001 2.40999997e-001 678 | 2.41999999e-001 2.38499999e-001 2.35300004e-001 2.33799994e-001 679 | 2.32700005e-001 2.31099993e-001 2.28699997e-001 2.27799997e-001 680 | 2.28100002e-001 2.29399994e-001 2.28699997e-001 2.27300003e-001 681 | 2.32099995e-001 2.30800003e-001 2.31999993e-001 2.28499994e-001 682 | 2.26899996e-001 2.23499998e-001 2.21100003e-001 2.20599994e-001 683 | 2.19500005e-001 2.17500001e-001 2.06000000e-001 2.13100001e-001 684 | 2.12200001e-001 2.11999997e-001 2.09299996e-001 2.08199993e-001 685 | 2.04400003e-001 2.05899999e-001 686 | <_> 687 | 2.83800006e-001 2.87200004e-001 2.92699993e-001 2.95700014e-001 688 | 2.95199990e-001 3.01800013e-001 2.96099991e-001 3.04199994e-001 689 | 3.02300006e-001 2.98599988e-001 2.98500001e-001 2.98999995e-001 690 | 2.92600006e-001 2.89600015e-001 2.88500011e-001 2.89000005e-001 691 | 2.87800014e-001 2.84900010e-001 2.89999992e-001 2.89299995e-001 692 | 2.85800010e-001 2.89999992e-001 2.91599989e-001 2.94600010e-001 693 | 2.94800013e-001 2.94499993e-001 2.92899996e-001 2.92800009e-001 694 | 2.90699989e-001 2.88700014e-001 2.87600011e-001 2.87499994e-001 695 | 2.84399986e-001 2.84099996e-001 2.82400012e-001 2.81500012e-001 696 | 2.78800011e-001 2.78200001e-001 2.78899997e-001 2.77399987e-001 697 | 2.75299996e-001 2.72100002e-001 2.70099998e-001 2.69699991e-001 698 | 2.66900003e-001 2.66200006e-001 2.63399988e-001 2.60899991e-001 699 | 2.61200011e-001 2.59700000e-001 2.57200003e-001 2.56500006e-001 700 | 2.52499998e-001 2.43599996e-001 2.49699995e-001 2.48799995e-001 701 | 2.47299999e-001 2.48199999e-001 2.49200001e-001 2.46399999e-001 702 | 2.46099994e-001 2.43300006e-001 2.19200000e-001 2.40700006e-001 703 | 2.39399999e-001 2.36300007e-001 2.31900007e-001 2.31700003e-001 704 | 2.27599993e-001 2.27500007e-001 705 | <_> 706 | 3.05200011e-001 3.11300009e-001 3.14799994e-001 3.18399996e-001 707 | 3.20800006e-001 3.21500003e-001 3.24800014e-001 3.23399991e-001 708 | 3.23799998e-001 3.23199987e-001 3.22800010e-001 3.19499999e-001 709 | 3.19400012e-001 3.19299996e-001 3.19499999e-001 3.19099993e-001 710 | 3.18300009e-001 3.19499999e-001 3.17600012e-001 3.18300009e-001 711 | 3.20899993e-001 3.18899989e-001 3.17699999e-001 3.24000001e-001 712 | 3.20600003e-001 3.20899993e-001 3.19000006e-001 3.20499986e-001 713 | 3.17400008e-001 3.14999998e-001 3.14300001e-001 3.13300014e-001 714 | 3.10299993e-001 3.09399992e-001 3.08099985e-001 3.06400001e-001 715 | 3.03600013e-001 3.03200006e-001 3.03499997e-001 3.02300006e-001 716 | 3.00999999e-001 2.97399998e-001 2.95399994e-001 2.93900013e-001 717 | 2.91500002e-001 2.89799988e-001 2.86900014e-001 2.86100000e-001 718 | 2.85600007e-001 2.85299987e-001 2.85899997e-001 2.84000009e-001 719 | 2.91999996e-001 2.96000004e-001 2.90399998e-001 2.87400007e-001 720 | 2.86599994e-001 2.79399991e-001 2.78200001e-001 2.77900010e-001 721 | 2.75299996e-001 2.72199988e-001 2.76600003e-001 2.67300010e-001 722 | 2.66200006e-001 2.64200002e-001 2.64600009e-001 2.61500001e-001 723 | 2.56999999e-001 2.58100003e-001 724 | <_> 725 | 1.94499999e-001 1.95700005e-001 1.96099997e-001 1.97099999e-001 726 | 1.96999997e-001 1.95099995e-001 1.96500003e-001 1.92499995e-001 727 | 1.89300001e-001 1.89500004e-001 1.85800001e-001 1.83799997e-001 728 | 1.81299999e-001 1.80999994e-001 1.80000007e-001 1.78200006e-001 729 | 1.76400006e-001 1.77300006e-001 1.75899997e-001 1.75200000e-001 730 | 1.76899999e-001 1.73299998e-001 1.71100006e-001 1.74999997e-001 731 | 1.74300000e-001 1.73500001e-001 1.71299994e-001 1.70000002e-001 732 | 1.69100001e-001 1.66600004e-001 1.66800007e-001 1.66500002e-001 733 | 1.64100006e-001 1.64900005e-001 1.63399994e-001 1.61599994e-001 734 | 1.59999996e-001 1.60699993e-001 1.60500005e-001 1.59600005e-001 735 | 1.59600005e-001 1.56399995e-001 1.55499995e-001 1.54899999e-001 736 | 1.53500006e-001 1.52799994e-001 1.51999995e-001 1.51500002e-001 737 | 1.50500000e-001 1.48800001e-001 1.49000004e-001 1.48699999e-001 738 | 1.53600007e-001 1.58299997e-001 1.52099997e-001 1.49800003e-001 739 | 1.49200007e-001 1.45899996e-001 1.44700006e-001 1.44400001e-001 740 | 1.43700004e-001 1.41499996e-001 1.47900000e-001 1.38799995e-001 741 | 1.38799995e-001 1.37300000e-001 1.35800004e-001 1.34399995e-001 742 | 1.31500006e-001 1.32400006e-001 743 | <_> 744 | 2.20400006e-001 2.19699994e-001 2.25600004e-001 2.31399998e-001 745 | 2.27500007e-001 2.32899994e-001 2.27200001e-001 2.37800002e-001 746 | 2.36599997e-001 2.30800003e-001 2.31600001e-001 2.32500002e-001 747 | 2.25199997e-001 2.20400006e-001 2.21399993e-001 2.21000001e-001 748 | 2.20899999e-001 2.14599997e-001 2.19999999e-001 2.22100005e-001 749 | 2.15200007e-001 2.22000003e-001 2.25600004e-001 2.26099998e-001 750 | 2.25299999e-001 2.26300001e-001 2.25500003e-001 2.26400003e-001 751 | 2.24099994e-001 2.22900003e-001 2.20400006e-001 2.22000003e-001 752 | 2.19500005e-001 2.19099998e-001 2.17299998e-001 2.16900006e-001 753 | 2.15900004e-001 2.14200005e-001 2.14499995e-001 2.14599997e-001 754 | 2.12200001e-001 2.10099995e-001 2.09199995e-001 2.07699999e-001 755 | 2.05799997e-001 2.04899997e-001 2.03199998e-001 2.01700002e-001 756 | 2.00000003e-001 1.99300006e-001 1.98100001e-001 1.96999997e-001 757 | 1.91599995e-001 1.78299993e-001 1.84900001e-001 1.85299993e-001 758 | 1.84000000e-001 1.89099997e-001 1.90099999e-001 1.88199997e-001 759 | 1.88800007e-001 1.87199995e-001 1.60099998e-001 1.85200006e-001 760 | 1.84000000e-001 1.82300001e-001 1.75500005e-001 1.77800000e-001 761 | 1.74899995e-001 1.73999995e-001 762 | <_> 763 | 2.87699997e-001 2.90800005e-001 2.92699993e-001 2.94900000e-001 764 | 2.95199990e-001 2.96700001e-001 2.96099991e-001 2.96099991e-001 765 | 2.94400007e-001 2.93199986e-001 2.91200012e-001 2.89099991e-001 766 | 2.85200000e-001 2.83800006e-001 2.82299995e-001 2.81599998e-001 767 | 2.80299991e-001 2.78100014e-001 2.79199988e-001 2.78600007e-001 768 | 2.77399987e-001 2.77000010e-001 2.77200013e-001 2.81199992e-001 769 | 2.78400004e-001 2.78800011e-001 2.77300000e-001 2.76699990e-001 770 | 2.73999989e-001 2.70399988e-001 2.68900007e-001 2.68200010e-001 771 | 2.66099989e-001 2.66099989e-001 2.64200002e-001 2.62400001e-001 772 | 2.59299994e-001 2.57800013e-001 2.57600009e-001 2.56300002e-001 773 | 2.53899992e-001 2.51599997e-001 2.47999996e-001 2.46800005e-001 774 | 2.44700000e-001 2.43399993e-001 2.40999997e-001 2.38800004e-001 775 | 2.38499999e-001 2.35799998e-001 2.34599993e-001 2.34300002e-001 776 | 2.33899996e-001 2.28599995e-001 2.29800001e-001 2.27300003e-001 777 | 2.26699993e-001 2.24999994e-001 2.24600002e-001 2.21900001e-001 778 | 2.21200004e-001 2.19600007e-001 2.14399993e-001 2.15000004e-001 779 | 2.14100003e-001 2.11899996e-001 2.08000004e-001 2.07599998e-001 780 | 2.04799995e-001 2.03099996e-001 781 | <_> 782 | 1.65099993e-001 1.70100003e-001 1.65800005e-001 1.65099993e-001 783 | 1.70599997e-001 1.64900005e-001 1.73299998e-001 1.64600000e-001 784 | 1.65399998e-001 1.69699997e-001 1.68699995e-001 1.65000007e-001 785 | 1.68799996e-001 1.68899998e-001 1.71000004e-001 1.69599995e-001 786 | 1.68799996e-001 1.69699997e-001 1.67899996e-001 1.66899994e-001 787 | 1.69699997e-001 1.65399998e-001 1.62499994e-001 1.66600004e-001 788 | 1.65399998e-001 1.64000005e-001 1.64700001e-001 1.64499998e-001 789 | 1.62699997e-001 1.60899997e-001 1.62499994e-001 1.61599994e-001 790 | 1.59600005e-001 1.59999996e-001 1.59299999e-001 1.57000005e-001 791 | 1.55699998e-001 1.56100005e-001 1.56800002e-001 1.55499995e-001 792 | 1.56700000e-001 1.54699996e-001 1.52199998e-001 1.52999997e-001 793 | 1.52300000e-001 1.51600003e-001 1.49599999e-001 1.50399998e-001 794 | 1.51099995e-001 1.50199994e-001 1.49800003e-001 1.48499995e-001 795 | 1.56499997e-001 1.60099998e-001 1.55100003e-001 1.52500004e-001 796 | 1.52099997e-001 1.49000004e-001 1.48000002e-001 1.47799999e-001 797 | 1.46200001e-001 1.45400003e-001 1.51099995e-001 1.43900007e-001 798 | 1.42700002e-001 1.41900003e-001 1.41700000e-001 1.41100004e-001 799 | 1.38600007e-001 1.38699993e-001 800 | <_> 801 | 2.28300005e-001 2.35699996e-001 2.44399995e-001 2.50999987e-001 802 | 2.58899987e-001 2.68000007e-001 2.75700003e-001 2.83199996e-001 803 | 2.90600002e-001 2.99199998e-001 3.04699987e-001 3.10699999e-001 804 | 3.16000015e-001 3.19799989e-001 3.22899997e-001 3.26099992e-001 805 | 3.27499986e-001 3.28799993e-001 3.30799997e-001 3.31800014e-001 806 | 3.33499998e-001 3.35399985e-001 3.35099995e-001 3.39800000e-001 807 | 3.41300011e-001 3.42299998e-001 3.43899995e-001 3.47099990e-001 808 | 3.45400006e-001 3.43100011e-001 3.42799991e-001 3.43499988e-001 809 | 3.39500010e-001 3.39399993e-001 3.39700013e-001 3.38800013e-001 810 | 3.35900009e-001 3.35999995e-001 3.36299986e-001 3.35500002e-001 811 | 3.35900009e-001 3.33499998e-001 3.29899997e-001 3.30300003e-001 812 | 3.27300012e-001 3.26099992e-001 3.25300008e-001 3.23500007e-001 813 | 3.24000001e-001 3.23900014e-001 3.23799998e-001 3.23199987e-001 814 | 3.29699993e-001 3.32500011e-001 3.31099987e-001 3.26000005e-001 815 | 3.26000005e-001 3.22600007e-001 3.22100013e-001 3.21900010e-001 816 | 3.19799989e-001 3.19099993e-001 3.16700011e-001 3.14700007e-001 817 | 3.13899994e-001 3.13199997e-001 3.13100010e-001 3.10499996e-001 818 | 3.07099998e-001 3.08699995e-001 819 | <_> 820 | 3.54099989e-001 3.60100001e-001 3.63999993e-001 3.65399987e-001 821 | 3.68999988e-001 3.66299987e-001 3.67100000e-001 3.66400003e-001 822 | 3.66299987e-001 3.65099996e-001 3.61999989e-001 3.60500008e-001 823 | 3.58799994e-001 3.56500000e-001 3.54900002e-001 3.53300005e-001 824 | 3.51399988e-001 3.49000007e-001 3.50400001e-001 3.49500000e-001 825 | 3.48300010e-001 3.49700004e-001 3.48399997e-001 3.55199993e-001 826 | 3.53199989e-001 3.52100015e-001 3.51000011e-001 3.50499988e-001 827 | 3.47900003e-001 3.44700009e-001 3.42099994e-001 3.40299994e-001 828 | 3.38200003e-001 3.35799992e-001 3.34399998e-001 3.32100004e-001 829 | 3.28299999e-001 3.27199996e-001 3.25800002e-001 3.24900001e-001 830 | 3.23500007e-001 3.21099997e-001 3.15499991e-001 3.14799994e-001 831 | 3.11199993e-001 3.09100002e-001 3.07399988e-001 3.03999990e-001 832 | 3.05500001e-001 3.03299993e-001 3.03799987e-001 3.01200002e-001 833 | 3.04699987e-001 3.09100002e-001 3.06499988e-001 2.99199998e-001 834 | 2.98299998e-001 2.92299986e-001 2.90899992e-001 2.89200008e-001 835 | 2.86500007e-001 2.82999992e-001 2.71699995e-001 2.79100001e-001 836 | 2.75900006e-001 2.73699999e-001 2.72300005e-001 2.69300014e-001 837 | 2.65500009e-001 2.66200006e-001 838 | <_> 839 | 3.09399992e-001 3.13899994e-001 3.16599995e-001 3.16900015e-001 840 | 3.21000010e-001 3.19299996e-001 3.18599999e-001 3.17900002e-001 841 | 3.15899998e-001 3.15400004e-001 3.10499996e-001 3.07300001e-001 842 | 3.07999998e-001 3.05900007e-001 3.03999990e-001 3.01999986e-001 843 | 3.03600013e-001 3.01699996e-001 2.99800009e-001 2.98400015e-001 844 | 3.01899999e-001 3.00300002e-001 2.98000008e-001 3.04800004e-001 845 | 3.02100003e-001 3.01899999e-001 3.00599992e-001 2.99600005e-001 846 | 2.97399998e-001 2.94099987e-001 2.91500002e-001 2.93700010e-001 847 | 2.89200008e-001 2.88700014e-001 2.86700010e-001 2.86300004e-001 848 | 2.82599986e-001 2.81300008e-001 2.81399995e-001 2.79100001e-001 849 | 2.78400004e-001 2.75700003e-001 2.73499995e-001 2.71299988e-001 850 | 2.69699991e-001 2.67800003e-001 2.65799999e-001 2.62300014e-001 851 | 2.63500005e-001 2.63099998e-001 2.61000007e-001 2.59499997e-001 852 | 2.66400009e-001 2.75400013e-001 2.66900003e-001 2.64099985e-001 853 | 2.63999999e-001 2.57299989e-001 2.54999995e-001 2.55699992e-001 854 | 2.51700014e-001 2.48799995e-001 2.63599992e-001 2.43200004e-001 855 | 2.42300004e-001 2.39999995e-001 2.42100000e-001 2.38199994e-001 856 | 2.33899996e-001 2.33799994e-001 857 | 858 | <_> 859 | 44 860 | 3.6368637591373493e-001 861 | 862 | 6.2500000000000000e+001 6.2500000000000000e+001 863 | 6.2500000000000000e+001 6.2500000000000000e+001 864 | 6.2500000000000000e+001 6.8267523792068541e-001 865 | 3.3644777994409329e+000 6.2500000000000000e+001 866 | 6.2500000000000000e+001 6.2500000000000000e+001 867 | 7.5216612732264041e+000 6.2500000000000000e+001 868 | 6.2500000000000000e+001 6.2500000000000000e+001 869 | 6.2500000000000000e+001 6.2500000000000000e+001 870 | 6.2500000000000000e+001 6.9913673726002363e+000 871 | 6.2500000000000000e+001 6.2500000000000000e+001 872 | 1.6616650508102499e+001 6.2500000000000000e+001 873 | 6.2500000000000000e+001 1.2064136324254420e+000 874 | -6.2500000000000000e+001 -6.2500000000000000e+001 875 | -6.2500000000000000e+001 -6.2500000000000000e+001 876 | -6.2500000000000000e+001 -6.2500000000000000e+001 877 | -6.2500000000000000e+001 -1.9003918047506506e+001 878 | -5.3639637718653283e+001 -6.2500000000000000e+001 879 | -6.2500000000000000e+001 -6.2500000000000000e+001 880 | -6.2500000000000000e+001 -6.2500000000000000e+001 881 | -6.2500000000000000e+001 -2.6239690057556395e+001 882 | -6.2500000000000000e+001 -6.2500000000000000e+001 883 | -6.2500000000000000e+001 -6.2500000000000000e+001 884 | 885 | 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 886 | 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 887 | 888 | --------------------------------------------------------------------------------