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open source pkg v1

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This commit is contained in:
Vijay Yadev
2020-08-04 19:12:31 -04:00
parent bef213dba9
commit c389fc2c47
3708 changed files with 1624220 additions and 1 deletions
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pkg/OpenFace/matlab_version/CCNF/CalculateSimilarities_sparsity.m Normal file
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function [ Similarities, PrecalcQ2s, PrecalcQ2sFlat, PrecalcYqDs ] = CalculateSimilarities_sparsity( n_sequences, x, similarityFNs, sparsityFNs, y, const)
%CALCULATESIMILARITIES Summary of this function goes here
% Detailed explanation goes here
K = numel(similarityFNs);
K2 = numel(sparsityFNs);
%calculate similarity measures for each of the sequences
Similarities = cell(n_sequences, 1);
PrecalcQ2s = cell(n_sequences,1);
PrecalcQ2sFlat = cell(n_sequences,1);
PrecalcYqDs = zeros(n_sequences, K + K2);
if(iscell(x))
for q = 1 : n_sequences
xq = x{q};
n = size(xq, 1);
Similarities{q} = zeros([n, n, K+K2]);
PrecalcQ2s{q} = cell(K+K2,1);
PrecalcQ2sFlat{q} = zeros((n*(n+1))/2,K+K2);
% go over all of the similarity metrics and construct the
% similarity matrices
if(nargin > 4)
yq = y{q};
end
for k=1:K
Similarities{q}(:,:,k) = similarityFNs{k}(xq);
S = Similarities{q}(:,:,k);
D = diag(sum(S));
% PrecalcQ2s{q}(:,:,k) = D - S;
PrecalcQ2s{q}{k} = D - S;
B = D - S;
% PrecalcQ2sFlat{q}{k} = PrecalcQ2s{q}{k}(logical(tril(ones(size(S)))));
PrecalcQ2sFlat{q}(:,k) = B(logical(tril(ones(size(S)))));
if(nargin > 4)
PrecalcYqDs(q,k) = -yq'*B*yq;
end
end
for k=1:K2
Similarities{q}(:,:,K+k) = sparsityFNs{k}(xq);
S = Similarities{q}(:,:,K+k);
D = diag(sum(S));
% PrecalcQ2s{q}(:,:,k) = D - S;
PrecalcQ2s{q}{K+k} = D + S;
B = D + S;
% PrecalcQ2sFlat{q}{k} = PrecalcQ2s{q}{k}(logical(tril(ones(size(S)))));
PrecalcQ2sFlat{q}(:,K+k) = B(logical(tril(ones(size(S)))));
if(nargin > 4)
PrecalcYqDs(q,K+k) = -yq'*B*yq;
end
end
end
elseif(~const)
sample_length = size(x,2)/n_sequences;
similarities = cell(K, 1);
sparsities = cell(K2, 1);
for q = 1 : n_sequences
beg_ind = (q-1)*sample_length + 1;
end_ind = q*sample_length;
% don't take the bias term
xq = x(2:end, beg_ind:end_ind);
Similarities{q} = zeros([sample_length, sample_length, K+K2]);
PrecalcQ2s{q} = cell(K+K2,1);
PrecalcQ2sFlat{q} = zeros((sample_length*(sample_length+1))/2,K+K2);
% go over all of the similarity metrics and construct the
% similarity matrices
if(nargin > 4)
yq = y(:,q);
end
for k=1:K
if(q==1)
similarities{k} = similarityFNs{k}(xq);
end
Similarities{q}(:,:,k) = similarities{k};
S = Similarities{q}(:,:,k);
D = diag(sum(S));
% PrecalcQ2s{q}(:,:,k) = D - S;
PrecalcQ2s{q}{k} = D - S;
B = D - S;
% PrecalcQ2sFlat{q}{k} = PrecalcQ2s{q}{k}(logical(tril(ones(size(S)))));
PrecalcQ2sFlat{q}(:,k) = B(logical(tril(ones(size(S)))));
if(nargin > 4)
PrecalcYqDs(q,k) = -yq'*B*yq;
end
end
for k=1:K2
% this is constant so don't need to recalc
if(q==1)
sparsities{k} = sparsityFNs{k}(xq);
end
Similarities{q}(:,:,K+k) = sparsities{k};
S = Similarities{q}(:,:,K+k);
D = diag(sum(S));
% PrecalcQ2s{q}(:,:,k) = D - S;
PrecalcQ2s{q}{K+k} = D + S;
B = D + S;
% PrecalcQ2sFlat{q}{k} = PrecalcQ2s{q}{k}(logical(tril(ones(size(S)))));
PrecalcQ2sFlat{q}(:,K+k) = B(logical(tril(ones(size(S)))));
if(nargin > 4)
PrecalcYqDs(q,K+k) = -yq'*B*yq;
end
end
end
else
sample_length = size(x,2)/n_sequences;
similarities = cell(K, 1);
sparsities = cell(K2, 1);
PrecalcQ2s = {cell(K+K2,1)};
PrecalcQ2sFlat = {zeros((sample_length*(sample_length+1))/2,K+K2)};
Similarities = {zeros([sample_length, sample_length, K+K2])};
beg_ind = 1;
end_ind = sample_length;
% don't take the bias term
xq = x(2:end, beg_ind:end_ind);
% go over all of the similarity metrics and construct the
% similarity matrices
for k=1:K
similarities{k} = similarityFNs{k}(xq');
Similarities{1}(:,:,k) = similarities{k};
S = Similarities{1}(:,:,k);
D = diag(sum(S));
PrecalcQ2s{1}{k} = D - S;
B = D - S;
% flatten the symmetric matrix to save space
PrecalcQ2sFlat{1}(:,k) = B(logical(tril(ones(size(S)))));
if(nargin > 4)
PrecalcYqDs(:,k) = diag(-y'*B*y);
end
end
for k=1:K2
% this is constant so don't need to recalc
sparsities{k} = sparsityFNs{k}(xq');
Similarities{1}(:,:,K+k) = sparsities{k};
S = Similarities{1}(:,:,K+k);
D = diag(sum(S));
% PrecalcQ2s{q}(:,:,k) = D - S;
PrecalcQ2s{1}{K+k} = D + S;
B = D + S;
% PrecalcQ2sFlat{q}{k} = PrecalcQ2s{q}{k}(logical(tril(ones(size(S)))));
PrecalcQ2sFlat{1}(:,K+k) = B(logical(tril(ones(size(S)))));
if(nargin > 4)
PrecalcYqDs(:,K+k) = diag(-y'*B*y);
end
end
end
end