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remove dependency on line codes

master
Iraj 5 years ago
parent
45b0af2add
commit
586ba6a69c
8 changed files with 284 additions and 81 deletions
Split View Show Diff Stats
  1. 0
    2
      MissingNodes_SrcCode2014/DumpDataset.m
  2. 5
    6
      MissingNodes_SrcCode2014/MissingNodes_S8b.m
  3. 7
    7
      MissingNodes_SrcCode2014/PrepareAttributes5.m
  4. 218
    4
      MissingNodes_SrcCode2014/RemoveRandomNodesWithImages.m
  5. 53
    61
      MissingNodes_SrcCode2014/RunExpSrv8s.m
  6. 1
    1
      MissingNodes_SrcCode2014/RunGraphEditDist.m
  7. BIN
      graph_production/produced_graphs/testgraph_45_133__.txt.mat
  8. BIN
      graph_production/produced_graphs/testgraph_45_133__.usr.mat

+ 0
- 2
MissingNodes_SrcCode2014/DumpDataset.m View File

@@ -29,5 +29,3 @@ for i=1:m
LogMsg(sprintf('DumpDataset for %s%s iter %d/%d into %s%s.', dataFilePath, dataFileName, iter, i, outPath, outFile));
end
%fprintf('Completed DumpDataset.\n');

+ 5
- 6
MissingNodes_SrcCode2014/MissingNodes_S8b.m View File

@@ -682,6 +682,7 @@ function [ rand_score, purity, p_triads, missing_nodes_mapping, removed_nodes] =
% cases we are using the GED as the main measure
fprintf('^%s', newPredictedGraph)
%remap the original data so that the known nodes match the
%predicted data and the missing nodes match the predicted
% nodes created from each cluster
@@ -712,8 +713,7 @@ function [ rand_score, purity, p_triads, missing_nodes_mapping, removed_nodes] =
small_data4 = newPredictedGraph;
small_data4(indices_to_remove,:) = [];
small_data4(:,indices_to_remove) = [];
%%% Sigal 27.1.13 - save reduce graphs for GED
Sigal 27.1.13 - save reduce graphs for GED
if dumpSmallFlag == 1
saveSmallData(dumpSmallDataPath, dataFileName, iter, affinity_calculation_type, withAttrWeight, num_missing_nodes, small_data, 1);
saveSmallData(dumpSmallDataPath, dataFileName, iter, affinity_calculation_type, withAttrWeight, num_missing_nodes, small_data2, 2);
@@ -767,8 +767,7 @@ function [ rand_score, purity, p_triads, missing_nodes_mapping, removed_nodes] =
small_data2(:,indices_to_remove) = [];
%fprintf('&%s', small_data2)
%fprintf('&&%s', small_data)
%%% Sigal 27.1.13 - save reduce graphs for GED
Sigal 27.1.13 - save reduce graphs for GED
withAttrWeight = origWithAttrWeight+1000*(1-percent_known_placeholders)*10;
if dumpSmallFlag == 1
saveSmallData(dumpSmallDataPath, dataFileName, iter, affinity_calculation_type, withAttrWeight, num_missing_nodes, small_data, 1);
@@ -930,6 +929,7 @@ end %main function
% sigal - 29.10.13
% calc only PHs affinity
function [affinity] = CalcPHsAffinity( data, affType, actual_graph_size, num_missing_nodes, num_attr_nodes, attWeight, addMissingAtt)
global affinity_calculation_shortest_path;
global affinity_calculation_euclid;
@@ -1320,7 +1320,7 @@ end % function BuildTrueClustering
function saveSmallData(dumpSmallDataPath, dataFileName, iter, affinity_type, withAttr, missNodes, small_data, i)
%%% Sigal 24.1.13 - TODO
Sigal 24.1.13 - TODO
outFile = sprintf('%s_%d_%d_%d_%d_small_data_%d', dataFileName, iter, missNodes, affinity_type, withAttr, i);
if affinity_type == 9 % save instead a dummy size (1) and the best_alg
SaveIntMatrixToFile(strcat(dumpSmallDataPath, outFile,'_edges.txt'), small_data, 1);
@@ -1411,4 +1411,3 @@ function [sumPurity] = CalcSumPurity(clusteringResults, indices)
end
sumPurity = sum(crossPurity,2);
end % function CalcSumPurity

+ 7
- 7
MissingNodes_SrcCode2014/PrepareAttributes5.m View File

@@ -1,6 +1,6 @@
% Map attribute to categories
% Use 0 for null/no value, 1 for unknown/private value (if exist) and then real values
function [outAttributes, attUpperRange, selectedAttr, attStat] = PrepareAttributes5(dataFilePath, dataFileName, numNodes, expectedAttrCols, maxAttStat, inSelectedAttr, debug, debugPath)
Map attribute to categories
Use 0 for null/no value, 1 for unknown/private value (if exist) and then real values
function [outAttributes, attUpperRange, selectedAttr, attStat] = PrepareAttributes5(dataFilePath, dataFileName, expectedAttrCols, maxAttStat, inSelectedAttr, debug, debugPath)
outNoneValue = 0;
@@ -45,8 +45,8 @@ load(strcat(dataFilePath, dataFileName), 'attributes');
m = size(attributes,1); % num nodes/lines
n = size(attributes,2); % num attributes/cols
if n ~= expectedAttrCols || m ~= numNodes
fprintf('PrepareAttributes - Invalid size: expecting (%dx%d), got (%dx%d)\n',numNodes,expectedAttrCols,m,n);
if n ~= expectedAttrCols
fprintf('PrepareAttributes - Invalid size: expecting (%d), got (%dx%d)\n',expectedAttrCols,m,n);
return;
end
@@ -79,7 +79,7 @@ if selectedAttr(countryCol) == 1
for i=1:maxCountry
indices = (new_values==i);
count = sum(indices);
if count/numNodes > maxAttStat
if count/m > maxAttStat
new_values(indices) = 0;
end
end
@@ -108,7 +108,7 @@ end
% calculate statistics and filter according to zero & maxAttStat
attStat = zeros(1,n);
for a = 1:n
attStat(a) = nnz(outAttributes(:,a))/numNodes;
attStat(a) = nnz(outAttributes(:,a))/m;
if attStat(a) == 0
selectedAttr(a)=0;
elseif selectedAttr(a)>0 && attStat(a) > maxAttStat && a>1 % don't filter country

+ 218
- 4
MissingNodes_SrcCode2014/RemoveRandomNodesWithImages.m View File

@@ -48,6 +48,7 @@ function [ data, attData, missing_nodes_mapping ] = RemoveRandomNodesWithImages(
for i = missing_nodes_all_neighbors
neighbors = find(data(i,:));
missing_neighbors = intersect(neighbors, missing_nodes_list);
missing_neighbors = sort(missing_neighbors, 'descend');
for curr_missing_neighbor = missing_neighbors
if data(i,curr_missing_neighbor) == 1
@@ -103,6 +104,213 @@ function [ data, attData, missing_nodes_mapping ] = RemoveRandomNodesWithImages(
end %function RemoveRandomNodes3
%sigal - move old implementation to function
function [missing_nodes] = ChooseMissingNodes(num_nodes_to_remove, data, attData, totalAttNum, numAttPerPH, missing_nodes_mapping, numImagesProfiles)
missing_nodes_all_neighbors = zeros(1, size(data,2));
%randomize a list of nodes to remove and sort it
if size(missing_nodes_mapping,1)> 0
%Sigal 23.1.14 - second row is the profile mapping
missing_nodes = missing_nodes_mapping(1:2,:); %%sort(missing_nodes_mapping(1,:) , 2, 'descend');
%find all missing node neighbors
for curr_missing_node = missing_nodes(1,:) % first row is the removed nodes
missing_nodes_all_neighbors = missing_nodes_all_neighbors | data(curr_missing_node,:);
missing_nodes_all_neighbors(1,curr_missing_node)=1;
end
else
missing_nodes = [];
end
% outlier1 - nodes with only one edge
numEdges = sum(data,1);
invalidNodes1a = (numEdges==1); %%numEdges<3); %%(numEdges==1);
missing_nodes_all_neighbors(1,invalidNodes1a) = 1;
%invalidNodes1b = (numEdges>7); %% 6.13 (mem issues) use 7
%invalidNodes1b = (numEdges>15); %%25); %%(numEdges==1); %% sigal - 6.2.13 max=15 (sarit)
%invalidNodes1b = (numEdges>8); %%15); %% sigal/sarit - 9.12.13 max=8
%missing_nodes_all_neighbors(1,invalidNodes1b) = 1;
% outlier2 - nodes with less than numAttPerPH attributes
% sigal 31.1.14 - support remove without attributes
if totalAttNum > 0 && numAttPerPH > 0
numAttr = sum(attData,2)';
invalidNodes2 = (numAttr<numAttPerPH);
missing_nodes_all_neighbors(1,invalidNodes2) = 1;
else
invalidNodes2 = zeros(1,size(invalidNodes1a,2));
end
% outlier statistics
count = nnz(invalidNodes1a|invalidNodes2);
if count*1.5 > size(data,2)
fprintf('RemoveRandomNodes2: too many outliers nodes %d.\n',count);
end
%sigal - 23.1.14 - choose image profile
imagesProfiles = 1:1:numImagesProfiles;
if size(missing_nodes,1)> 0
usedProfiles = missing_nodes(2,:);
imagesProfiles(usedProfiles)=[];
end
for i=1:num_nodes_to_remove
valid_nodes = find(missing_nodes_all_neighbors~=1);
if(size(valid_nodes,2) < 1)
fprintf('Full Graph')
end
inx = ceil(rand(1)*size(valid_nodes,2));
node = valid_nodes(inx);
%sigal - 23.1.14 - choose image profile
profile = ceil(rand(1)*size(imagesProfiles,2));
newNode = [node;imagesProfiles(profile)];
imagesProfiles(profile) = [];
% add selected node to missing_nodes list and update the all neighbors list
missing_nodes = [missing_nodes newNode];
missing_nodes_all_neighbors(1,node)=1;
missing_nodes_all_neighbors = missing_nodes_all_neighbors | data(node,:);
end
end %ChooseMissingNodes
% sigal - append col & row for the placeholder
function [data] = ExpandDataByOne(data, friend, non_neighbors_distance)
new_col = ones(size(data, 1), 1) * non_neighbors_distance;
new_col(friend) = 1;
data = [data new_col];
new_row = ones(1,size(data, 2)) * non_neighbors_distance;
new_row(friend) = 1;
data = [data; new_row];
data(size(data, 1), size(data,2)) = 0;
end %ExpandDataByOne
% sigal - append row for the placeholder
function [attData] = ExpandAttByOne(attData, orgNode, non_neighbors_distance, totalAttNum, numAttPerPH)
if totalAttNum>0 && numAttPerPH>0
attIndices = find(attData(orgNode, :)==1);
while size(attIndices,2) > numAttPerPH
inx = ceil(rand(1)*size(attIndices,2));
attIndices(:,inx) = [];
end
else
attIndices=[];
end
new_row = ones(1,size(attData, 2)) * non_neighbors_distance;
for i=1:size(attIndices,2)
new_row(i)=1;
end
attData = [attData; new_row];
end %ExpandAttByOne
function [ data, attData, missing_nodes_mapping ] = RemoveRandomNodesWithImages( data, attData, totalAttNum, num_missing_nodes, missing_nodes_mapping, numImagesProfiles, non_neighbors_distance, missingNodesInput )
%RemoveRandomNodes Remove num_missing_nodes from data. If some nodes are
%removed already, provide missing_nodes_mapping
% Detailed explanation goes here
if nargin < 7
non_neighbors_distance = 0;
end
if nargin >= 8 % i.e. getting missingNodesInput
missing_nodes = missingNodesInput;
else
%%data_orig = data;
numAttPerPH = 0;
% if the mapping is larger than the number of nodes we want to remove, empty
% it and start a new mapping. This can happen if we finished looping over
% the number of missing nodes and started a new iteration of an outer loop.
if size(missing_nodes_mapping,2) > num_missing_nodes
missing_nodes_mapping = [];
num_nodes_to_remove = num_missing_nodes;
else
num_nodes_to_remove = num_missing_nodes - size(missing_nodes_mapping,2);
end
% randomly choose missing nodes
%missing_nodes = ChooseMissingNodes_old(num_nodes_to_remove, data, missing_nodes_mapping, non_neighbors_distance);
missing_nodes = ChooseMissingNodes(num_nodes_to_remove, data, attData, totalAttNum, numAttPerPH, missing_nodes_mapping, numImagesProfiles);
%sort the list and create a list of the new nodes that each missing node is mapped to - each link
%to a missing node is replaced by a link to a new, "UNK" node
%Sigal 23.1.14 - missing_nodes is now matrix with two rows:
% first the removed node and second the selected profile)
% no need to call unique as validation is already done in ChooseMissingNodes
%missing_nodes = sort( unique(missing_nodes), 'descend');
%missing_nodes = sort( missing_nodes , 2, 'descend');
missing_nodes_mapping = missing_nodes;
missing_nodes_list = sort(missing_nodes(1,:),'descend');
%replace each link to a missing node with a link to a new node
%find all missing node neighbors
missing_nodes_all_neighbors = zeros(1, size(data,2));
for curr_nissing_node = missing_nodes_list
missing_nodes_all_neighbors = missing_nodes_all_neighbors | data(curr_nissing_node,:);
end
missing_nodes_all_neighbors = find(missing_nodes_all_neighbors);
%for each node in missing_nodes_all_neighbors add edges to placeholder
for i = missing_nodes_all_neighbors
neighbors = find(data(i,:));
missing_neighbors = intersect(neighbors, missing_nodes_list);
missing_neighbors = sort(missing_neighbors, 'descend');
for curr_missing_neighbor = missing_neighbors
if data(i,curr_missing_neighbor) == 1
% append col & row for the placeholder
data = ExpandDataByOne(data, i, non_neighbors_distance);
% sigal 31.1.14 - support remove without attributes
if totalAttNum > 0
attData = ExpandAttByOne(attData, curr_missing_neighbor, non_neighbors_distance, totalAttNum, numAttPerPH);
end
%add the new UNK node to the missing nodes mapping j is the index of the missing node
%look for the first zero in column j of the missing nodes mapping and put the new node
%index there
added_node = 0;
%add it in the first position which equals zero
%sigal 23.1.14 - find index according to actual structure (not sorted)
j = find( missing_nodes_mapping(1,:) == curr_missing_neighbor, 1);
for k = 1 : size(missing_nodes_mapping,1)
if missing_nodes_mapping(k, j) == 0
%if we start with 1000 nodes, and we have 5 missing nodes, after
%adding one node at this point, the size of the graph is 1001. 5 nodes
%will be removed so the correct index of the new node will be 1001 - 5 = 996.
%The next one is 997 and so on.
missing_nodes_mapping(k, j) = size(data,1) - num_missing_nodes;
added_node = 1;
break;
end
end
%if all the column is non-zero, add a new row and put it there
if added_node == 0
missing_nodes_mapping = [missing_nodes_mapping; zeros(1, size(missing_nodes_mapping,2))];
missing_nodes_mapping(size(missing_nodes_mapping,1), j) = size(data,1) - num_missing_nodes;
end
end %if friend
end %missing_neighbors
end %missing_nodes_all_neighbors
end % if getting missingNodesInput
%remove the missing nodes from the matrix (missing nodes MUST be sorted in descending order!!
%so that removing one does not affect the index of the others)
for j = 1:size(missing_nodes_list,2)
missing_node_idx = missing_nodes_list(j);
%remove column
data(:, missing_node_idx) = [];
%remove row
data(missing_node_idx, :) = [];
% sigal 31.1.14 - support remove without attributes
if totalAttNum > 0
attData(missing_node_idx, :) = [];
end
end
end %function RemoveRandomNodes3
%sigal - move old implementation to function
function [missing_nodes] = ChooseMissingNodes(num_nodes_to_remove, data, attData, totalAttNum, numAttPerPH, missing_nodes_mapping, numImagesProfiles)
missing_nodes_all_neighbors = zeros(1, size(data,2));
@@ -125,8 +333,8 @@ function [missing_nodes] = ChooseMissingNodes(num_nodes_to_remove, data, attData
missing_nodes_all_neighbors(1,invalidNodes1a) = 1;
%invalidNodes1b = (numEdges>7); %% 6.13 (mem issues) use 7
%invalidNodes1b = (numEdges>15); %%25); %%(numEdges==1); %% sigal - 6.2.13 max=15 (sarit)
invalidNodes1b = (numEdges>8); %%15); %% sigal/sarit - 9.12.13 max=8
missing_nodes_all_neighbors(1,invalidNodes1b) = 1;
%invalidNodes1b = (numEdges>8); %%15); %% sigal/sarit - 9.12.13 max=8
%missing_nodes_all_neighbors(1,invalidNodes1b) = 1;
% outlier2 - nodes with less than numAttPerPH attributes
% sigal 31.1.14 - support remove without attributes
if totalAttNum > 0 && numAttPerPH > 0
@@ -137,7 +345,7 @@ function [missing_nodes] = ChooseMissingNodes(num_nodes_to_remove, data, attData
invalidNodes2 = zeros(1,size(invalidNodes1a,2));
end
% outlier statistics
count = nnz(invalidNodes1a|invalidNodes1b|invalidNodes2);
count = nnz(invalidNodes1a|invalidNodes2);
if count*1.5 > size(data,2)
fprintf('RemoveRandomNodes2: too many outliers nodes %d.\n',count);
end
@@ -151,7 +359,12 @@ function [missing_nodes] = ChooseMissingNodes(num_nodes_to_remove, data, attData
for i=1:num_nodes_to_remove
valid_nodes = find(missing_nodes_all_neighbors~=1);
inx = ceil(rand(1)*size(valid_nodes,2));
if(size(valid_nodes,2) < 1)
fprintf('Full Graph')
end
inx = ceil(rand(1)*size(valid_nodes,2));
node = valid_nodes(inx);
%sigal - 23.1.14 - choose image profile
profile = ceil(rand(1)*size(imagesProfiles,2));
@@ -196,3 +409,4 @@ function [attData] = ExpandAttByOne(attData, orgNode, non_neighbors_distance, to
end %ExpandAttByOne

+ 53
- 61
MissingNodes_SrcCode2014/RunExpSrv8s.m View File

@@ -1,5 +1,5 @@
function [] = RunExpSrv8s(iterStartStr, iterEndStr, netSizeStr, addAttPercStr)
%function [] = RunExpSrv8s(iterStartStr, iterEndStr, netSizeStr, normF1, normF2, normF3, addAttPercStr)
function [] = RunExpSrv8s(iterStartStr, iterEndStr, netSizeStr, normF1, normF2, normF3, addAttPercStr)
function [] = RunExpSrv8s(iterStartStr, iterEndStr, addAttPercStr)
%affinity calculation types
% affinity_calculation_shortest_path = 0;
@@ -51,10 +51,9 @@ percentKnownPHsVec = 1;
% ds_100k = [20000 25000 50000 75000 100000];
% ds_100km = [100001];
% ds_Train = [2001 2049];
ds_GridTrain = [12];
ds_GridTrain = [45];
% input netSize
netSize = str2num(netSizeStr);
% if find(ds_10k==netSize)
% ds_str = 'Datasets_10K/';
% num_missing_nodes_arr = [10 20 30 50 70 100];%[10 100 150];
@@ -83,16 +82,14 @@ netSize = str2num(netSizeStr);
% fprintf('RunExpSrv8s:Invalid netSize %d.\n',netSize);
% return;
% end
ds_str = 'graph_production/produced_graphs/';
num_missing_nodes_arr = [1 2 3];
ds_str = 'in/';
num_missing_nodes_arr = [2];
fprintf('RunExpSrv8s: netSize %d, dataset %s\n',netSize,ds_str);
rootDir = '../';
%rootDir = '/Users/armin/Desktop/DML/projects/graphgenproj/'; %Facebook/';
%rootDir = 'D:/__SN_Jan14_FF75/'; %Facebook/';
filePrefix = 'testgraph_*'; % 'facebook_sparse_'; %
netSizes = netSize; %%[2048 4096 5000 8192 10000 16384 32768];
%Sigal - 13.2.14 - images data
imagesDir = strcat(rootDir,'Images/');
@@ -107,7 +104,7 @@ imgSimType = 1; %% 0=realData, 1=rand(uniform distribution), 2=randn(normal dist
datasetDir = strcat(rootDir,ds_str); %'Datasets_10K/'); %'Facebook/Datasets_10K/'); %'Traing_16K/'); %% TODO sigal - change rootDir path before EXE build
factor_str = sprintf('F%d%d%d_',normFactorVec);
images_str = sprintf('I%dP%dM%d_',imgSimType,imgSimProbDiff*10,imgMissProb*10);
results_dir = strcat(datasetDir,'testImg_noTh_',netSizeStr,'/',factor_str,images_str,'Iter_',iterStartStr,iterEndStr,'/');
results_dir = strcat(datasetDir,'testImg_noTh_','/',factor_str,images_str,'Iter_',iterStartStr,iterEndStr,'/');
fprintf('RunExpSrv8b: results_dir %s\n',results_dir);
runAlgFlag = 1;
@@ -139,13 +136,7 @@ LogMsg(sprintf('%s Start RunExpSrv8s RunExperiment (random %d, addMissingAtt %.2
% end
% LogMsg(sprintf('Select %d attributes out of %d ...', sum(attSelected), size(attSelected,2)));
for nodes = netSizes
fprintf('----------!!!!%d---------', nodes);
if size(strfind(filePrefix, 'facebook'),1)>0
prefix = sprintf('%s%s%d_%s',datasetDir,filePrefix,nodes,'*.mat'); % '0*.txt.mat');
else
prefix = sprintf('%s%s%d_%s',datasetDir,filePrefix,nodes,'*.txt.mat'); % '0*.txt.mat');
end
prefix = sprintf('%s%s_%s',datasetDir,filePrefix,'*.txt.mat'); % '0*.txt.mat');
files = dir(prefix);
firstIter = 1;
@@ -155,66 +146,67 @@ for nodes = netSizes
end
for i = 1:size(files,1) % loop over the list of networks
try
file = files(i).name;
file = files(i).name;
if size(strfind(filePrefix, 'facebook'),1)>0
LogMsg(sprintf('facebook netwrok, skipping attributes ...'));
attributes = [];
attUpperRange = [];
else
% sigal 12/6/13 - use binary attribute mat file
attFile = strrep(file, '.txt.mat', '.usr.mat');
[attributes, attUpperRange, attSelected, attStat] = PrepareAttributes5(datasetDir, attFile, nodes, numAttrCols, maxAttStat, attSelected);
[attributes, attUpperRange, attSelected, ~] = PrepareAttributes5(datasetDir, attFile, numAttrCols, maxAttStat, attSelected);
LogMsg(sprintf('Select %d attributes out of %d ...', sum(attSelected), size(attSelected,2)));
end
if runAlgFlag == 1
date_now = clock;
date_now = strcat(num2str(date_now(1)),'_',num2str(date_now(2)),'_', num2str(date_now(3)),'_', num2str(date_now(4)), num2str(date_now(5)),'_', num2str(date_now(6)));
% make sure dump & results directories exist
if (firstIter == 1 && i == 1)
firstIter = 0;
if isdir(results_dir) == 0
mkdir(results_dir);
if runAlgFlag == 1
date_now = clock;
date_now = strcat(num2str(date_now(1)),'_',num2str(date_now(2)),'_', num2str(date_now(3)),'_', num2str(date_now(4)), num2str(date_now(5)),'_', num2str(date_now(6)));
% make sure dump & results directories exist
if (firstIter == 1 && i == 1)
firstIter = 0;
if isdir(results_dir) == 0
mkdir(results_dir);
end
dumpFilePath = sprintf('%sdumpKronEM_%s/', results_dir, date_now);
if (dumpKronEM == 1)
mkdir(dumpFilePath);
end
dump_data_dir = sprintf('%sdumpData_%s/', results_dir, date_now);
if dumpGED == 1 && isdir(dump_data_dir) == 0
mkdir(dump_data_dir)
end
end
dumpFilePath = sprintf('%sdumpKronEM_%s/', results_dir, date_now);
if (dumpKronEM == 1)
mkdir(dumpFilePath);
% run algorithm (file load is done internaly)
[rand_score,purity,p_triads,missing_nodes_mapping,removed_nodes] = MissingNodes_S8b(datasetDir, file, ...
attributes, attUpperRange, attWeight, addMissingAtt, normFactorVec, affinities, num_missing_nodes_arr, attAffinityThreshold, ...
imagesData, numImagesProfiles, imgMissProb, imgSimType, imgSimProbDiff, percentKnownPHsVec, dumpGED, dump_data_dir, iter);
%[rand_score,purity,p_triads,missing_nodes_mapping,removed_nodes] = MissingNodes_Sparse(datasetDir, file, affinities, 1);
% dump graph data for KronEM runs
if dumpKronEM == 1
DumpDataset(datasetDir, file, iter, removed_nodes, dumpFilePath);
end
dump_data_dir = sprintf('%sdumpData_%s/', results_dir, date_now);
if dumpGED == 1 && isdir(dump_data_dir) == 0
mkdir(dump_data_dir)
% save results
out_file = sprintf('%sres_%s_%s.mat', results_dir, file, date_now);
save(out_file);
file_name = sprintf('%s_%s','../output/mine/',file);
if(iter == 0)
copyfile('../output/graphed_0.mat',file_name);
end
end
% run algorithm (file load is done internaly)
[rand_score,purity,p_triads,missing_nodes_mapping,removed_nodes] = MissingNodes_S8b(datasetDir, file, ...
attributes, attUpperRange, attWeight, addMissingAtt, normFactorVec, affinities, num_missing_nodes_arr, attAffinityThreshold, ...
imagesData, numImagesProfiles, imgMissProb, imgSimType, imgSimProbDiff, percentKnownPHsVec, dumpGED, dump_data_dir, iter);
%[rand_score,purity,p_triads,missing_nodes_mapping,removed_nodes] = MissingNodes_Sparse(datasetDir, file, affinities, 1);
% dump graph data for KronEM runs
if dumpKronEM == 1
DumpDataset(datasetDir, file, iter, removed_nodes, dumpFilePath);
end
% save results
out_file = sprintf('%sres_%s_%s.mat', results_dir, file, date_now);
save(out_file);
LogMsg(sprintf('Results for file %s,iter %d at %s',file,iter,out_file));
%fprintf('Completed RunExperiment cycle - results at %s.\n',out_file);
LogMsg(sprintf('Results for file %s,iter %d at %s',file,iter,out_file));
%fprintf('Completed RunExperiment cycle - results at %s.\n',out_file);
end
catch
fprintf('An Error Occured!!!!!')
end
% beep;
end
end
end
date_now = clock;
date_now = strcat(num2str(date_now(1)),'_',num2str(date_now(2)),'_', num2str(date_now(3)),'_', num2str(date_now(4)), num2str(date_now(5)),'_', num2str(date_now(6)));
LogMsg(sprintf('%s Completed RunExpSrv8s RunExperiment (random %d).',date_now,randSeed));
end
end

+ 1
- 1
MissingNodes_SrcCode2014/RunGraphEditDist.m View File

@@ -8,7 +8,7 @@ clc;
% calc GED vice the orignal graph
% end
%graphDist = [];
rootDir = '/Users/armin/Desktop/DML/projects/graphgenproj/graph_production/' ;
rootDir = 'C:\Users\Iraj\Desktop\DML\' ;
datasetDir = strcat(rootDir,'produced_graphs/'); %' '; % sigal 28.8.12
resultsDir = strcat(rootDir,'results_ged/');
file1Iter = []; % networks options [1:1:10] - selection iter

BIN
graph_production/produced_graphs/testgraph_45_133__.txt.mat View File


BIN
graph_production/produced_graphs/testgraph_45_133__.usr.mat View File


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