513 lines
16 KiB
R
513 lines
16 KiB
R
N <- 256
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x_matrix <- matrix(0, 128, 128)
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error_vector <- rep(0,128)
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x_num <- seq(1,num_experiments,1)
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#Iterate through each value in the s-vector representing number of sparse element in vector x
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for (i in x_num) {
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print(paste0("Running experiment number ",i))
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x_full <- rsparsematrix(512, 1, nnz = 512, rand.x=rnorm)
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#Create A matrix consisting of guassian distribution of i.i.d variables 256 (rows) X 512 (columns)
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A <- matrix(rnorm(N*M), nrow = N, ncol = M)
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for (s in s_vector) {
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x <- numeric(512)
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ind <- sample(1:512, s) # select s random locations which will have non zero values
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for (i in ind){
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x[i] <- x_full[i]
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}
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y <- A%*%x
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x0 <- t(A)%*%y
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xp <- l1eq_pd(x0,A,y)
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print(paste0(s,"/128 ","LHS: ",norm(xp-x,type='1'),"<=","RHS: ",10^-4*norm(x,type='2')," ",norm(xp-x,type='1') <= 10^-4*norm(x,type='2')))
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#10^-4*
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if (norm(xp-x,type='1') <= 10^-4*norm(x,type='2')) {
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error_vector[s]=1
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}
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else{
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error_vector[s]=0
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}
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}
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error_matrix[i,]=error_vector
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}
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#x <- numeric(512) #creates 512-dimenional vector of zeros
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#ind <- sample(1:512, s) #% select s random locations which will have non zero values
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#x[ind] <- A_row[c(ind)] #random non zero values % assign s random non zero values into selected locations
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x_full
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rsparsematrix(512, 1, density = [0,1],nnz = 512, rand.x=rnorm)
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rsparsematrix(512, 1, density = (0,1),nnz = 512, rand.x=rnorm)
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s_vector <- seq(1,128,1)
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source('l1eq_pd.R')
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num_experiments <- 4
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error_matrix <- matrix(0, num_experiments, 128)
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M <- 512
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N <- 256
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x_matrix <- matrix(0, 128, 128)
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error_vector <- rep(0,128)
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x_num <- seq(1,num_experiments,1)
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#Iterate through each experiement created
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for (i in x_num) {
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print(paste0("Running experiment number ",i))
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x_full <- rsparsematrix(512, 1, nnz = 512, rand.x=rnorm)
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#Create A matrix consisting of guassian distribution of i.i.d variables 256 (rows) X 512 (columns)
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A <- matrix(rnorm(N*M), nrow = N, ncol = M)
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#Iterate through each value in the s-vector representing number of sparse element in vector x
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for (s in s_vector) {
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x <- numeric(512)
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ind <- sample(1:512, s) # select s random locations which will have non zero values
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for (i in ind){
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x[i] <- x_full[i]
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}
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y <- A%*%x
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x0 <- t(A)%*%y
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xp <- l1eq_pd(x0,A,y)
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print(paste0(s,"/128 ","LHS: ",norm(xp-x,type='1'),"<=","RHS: ",10^-4*norm(x,type='2')," ",norm(xp-x,type='1') <= 10^-4*norm(x,type='2')))
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#10^-4*
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if (norm(xp-x,type='1') <= 10^-4*norm(x,type='2')) {
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error_vector[s]=1
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}
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else{
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error_vector[s]=0
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}
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}
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error_matrix[i,]=error_vector
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}
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#x <- numeric(512) #creates 512-dimenional vector of zeros
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#ind <- sample(1:512, s) #% select s random locations which will have non zero values
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#x[ind] <- A_row[c(ind)] #random non zero values % assign s random non zero values into selected locations
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s_vector <- seq(1,128,1)
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source('l1eq_pd.R')
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num_experiments <- 4
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error_matrix <- matrix(0, num_experiments, 128)
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M <- 512
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N <- 256
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x_matrix <- matrix(0, 128, 128)
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error_vector <- rep(0,128)
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x_num <- seq(1,num_experiments,1)
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#Iterate through each experiement created
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for (i in x_num) {
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print(paste0("Running experiment number ",i))
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x_full <- rsparsematrix(512, 1, nnz = 512, rand.x=rnorm)
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#Create A matrix consisting of guassian distribution of i.i.d variables 256 (rows) X 512 (columns)
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A <- matrix(rnorm(N*M), nrow = N, ncol = M)
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#Iterate through each value in the s-vector representing number of sparse element in vector x
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for (s in s_vector) {
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x <- numeric(512)
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ind <- sample(1:512, s) # select s random locations which will have non zero values
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for (i in ind){
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x[i] <- x_full[i]
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}
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y <- A%*%x
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x0 <- t(A)%*%y
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xp <- l1eq_pd(x0,A,y)
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print(paste0(s,"/128 ","LHS: ",norm(xp-x,type='1'),"<=","RHS: ",10^-4*norm(x,type='2')," ",norm(xp-x,type='1') <= 10^-4*norm(x,type='2')))
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#10^-4*
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if (norm(xp-x,type='1') <= 10^-4*norm(x,type='2')) {
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error_vector[s]=1
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}
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else{
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error_vector[s]=0
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}
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}
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error_matrix[i,]=error_vector
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}
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#x <- numeric(512) #creates 512-dimenional vector of zeros
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#ind <- sample(1:512, s) #% select s random locations which will have non zero values
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#x[ind] <- A_row[c(ind)] #random non zero values % assign s random non zero values into selected locations
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s_vector <- seq(1,128,1)
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source('l1eq_pd.R')
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num_experiments <- 4
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error_matrix <- matrix(0, num_experiments, 128)
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M <- 512
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N <- 256
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x_matrix <- matrix(0, 128, 128)
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error_vector <- rep(0,128)
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x_num <- seq(1,num_experiments,1)
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#Iterate through each experiement created
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for (i in x_num) {
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print(paste0("Running experiment number ",i))
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#x_full <- rsparsematrix(512, 1, nnz = 512, rand.x=rnorm)
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#Create A matrix consisting of guassian distribution of i.i.d variables 256 (rows) X 512 (columns)
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A <- matrix(rnorm(N*M), nrow = N, ncol = M)
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#Iterate through each value in the s-vector representing number of sparse element in vector x
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for (s in s_vector) {
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#x <- numeric(512)
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#ind <- sample(1:512, s) # select s random locations which will have non zero values
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#for (i in ind){
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# x[i] <- x_full[i]
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#}
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x <- rsparsematrix(512, 1, nnz = s, rand.x=rnorm)
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y <- A%*%x
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x0 <- t(A)%*%y
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xp <- l1eq_pd(x0,A,y)
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print(paste0(s,"/128 ","LHS: ",norm(xp-x,type='1'),"<=","RHS: ",10^-4*norm(x,type='2')," ",norm(xp-x,type='1') <= 10^-4*norm(x,type='2')))
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#10^-4*
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if (norm(xp-x,type='1') <= 10^-4*norm(x,type='2')) {
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error_vector[s]=1
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}
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else{
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error_vector[s]=0
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}
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}
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error_matrix[i,]=error_vector
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}
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x
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s_vector <- seq(1,128,1)
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source('l1eq_pd.R')
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num_experiments <- 4
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error_matrix <- matrix(0, num_experiments, 128)
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M <- 512
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N <- 256
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x_matrix <- matrix(0, 128, 128)
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error_vector <- rep(0,128)
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x_num <- seq(1,num_experiments,1)
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#Iterate through each experiement created
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for (i in x_num) {
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print(paste0("Running experiment number ",i))
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#x_full <- rsparsematrix(512, 1, nnz = 512, rand.x=rnorm)
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#Create A matrix consisting of guassian distribution of i.i.d variables 256 (rows) X 512 (columns)
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A <- matrix(rnorm(N*M), nrow = N, ncol = M)
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#Iterate through each value in the s-vector representing number of sparse element in vector x
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for (s in s_vector) {
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#x <- numeric(512)
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#ind <- sample(1:512, s) # select s random locations which will have non zero values
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#for (i in ind){
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# x[i] <- x_full[i]
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#}
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x <- rsparsematrix(512, 1, nnz = s, rand.x=rnorm)
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y <- A%*%x
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x0 <- t(A)%*%y
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xp <- l1eq_pd(x0,A,y)
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print(paste0(s,"/128 ","LHS: ",norm(xp-x,type='1'),"<=","RHS: ",10^-4*norm(x,type='2')," ",norm(xp-x,type='1') <= 10^-4*norm(x,type='2')))
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#10^-4*
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if (norm(xp-x,type='2') <= 10^-4*norm(x,type='2')) {
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error_vector[s]=1
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}
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else{
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error_vector[s]=0
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}
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}
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error_matrix[i,]=error_vector
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}
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plot(colMeans(error_matrix),type='l')
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s_vector <- seq(1,128,1)
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source('l1eq_pd.R')
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num_experiments <- 4
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error_matrix <- matrix(0, num_experiments, 128)
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M <- 512
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N <- 256
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x_matrix <- matrix(0, 128, 128)
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error_vector <- rep(0,128)
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x_num <- seq(1,num_experiments,1)
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#Iterate through each experiement created
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for (i in x_num) {
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print(paste0("Running experiment number ",i))
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x_full <- rsparsematrix(512, 1, nnz = 512, rand.x=rnorm)
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#Create A matrix consisting of guassian distribution of i.i.d variables 256 (rows) X 512 (columns)
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A <- matrix(rnorm(N*M), nrow = N, ncol = M)
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#Iterate through each value in the s-vector representing number of sparse element in vector x
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for (s in s_vector) {
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x <- numeric(512)
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ind <- sample(1:512, s) # select s random locations which will have non zero values
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for (i in ind){
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x[i] <- x_full[i]
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}
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#x <- rsparsematrix(512, 1, nnz = s, rand.x=rnorm)
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y <- A%*%x
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x0 <- t(A)%*%y
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xp <- l1eq_pd(x0,A,y)
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print(paste0(s,"/128 ","LHS: ",norm(xp-x,type='1'),"<=","RHS: ",10^-4*norm(x,type='2')," ",norm(xp-x,type='1') <= 10^-4*norm(x,type='2')))
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#10^-4*
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if (norm(xp-x,type='2') <= 10^-4*norm(x,type='2')) {
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error_vector[s]=1
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}
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else{
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error_vector[s]=0
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}
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}
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error_matrix[i,]=error_vector
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}
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help(norm)
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x
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len(x==TRUE)
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length(x==TRUE)
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length(x[x==TRUE])
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length(x[x==0])
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512-483
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x
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norm(xp-x,type='2')
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norm(x,type='2')
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s_vector <- seq(1,128,1)
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source('l1eq_pd.R')
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num_experiments <- 4
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error_matrix <- matrix(0, num_experiments, 128)
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M <- 512
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N <- 256
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x_matrix <- matrix(0, 128, 128)
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error_vector <- rep(0,128)
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x_num <- seq(1,num_experiments,1)
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#Iterate through each experiement created
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for (i in x_num) {
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print(paste0("Running experiment number ",i))
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x_full <- rsparsematrix(512, 1, nnz = 512, rand.x=rnorm)
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#Create A matrix consisting of guassian distribution of i.i.d variables 256 (rows) X 512 (columns)
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A <- matrix(rnorm(N*M), nrow = N, ncol = M)
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#Iterate through each value in the s-vector representing number of sparse element in vector x
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for (s in s_vector) {
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x <- numeric(512)
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ind <- sample(1:512, s) # select s random locations which will have non zero values
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for (i in ind){
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x[i] <- x_full[i]
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}
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#x <- rsparsematrix(512, 1, nnz = s, rand.x=rnorm)
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y <- A%*%x
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x0 <- t(A)%*%y
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xp <- l1eq_pd(x0,A,y)
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print(paste0(s,"/128 ","LHS: ",norm(xp-x,type='1'),"<=","RHS: ",10^-4*norm(x,type='2')," ",norm(xp-x,type='1') <= 10^-4*norm(x,type='2')))
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#10^-4*
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if (norm(xp-x,type='2') <= 10^-4*norm(x,type='2')) {
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error_vector[s]=1
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}
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else{
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error_vector[s]=0
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}
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}
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error_matrix[i,]=error_vector
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}
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norm(x,type='2')
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norm(xp-x,type='2')
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s_vector <- seq(1,128,1)
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source('l1eq_pd.R')
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num_experiments <- 4
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error_matrix <- matrix(0, num_experiments, 128)
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M <- 512
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N <- 256
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x_matrix <- matrix(0, 128, 128)
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error_vector <- rep(0,128)
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x_num <- seq(1,num_experiments,1)
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#Iterate through each experiement created
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for (i in x_num) {
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print(paste0("Running experiment number ",i))
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x_full <- rsparsematrix(512, 1, nnz = 512, rand.x=rnorm)
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#Create A matrix consisting of guassian distribution of i.i.d variables 256 (rows) X 512 (columns)
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A <- matrix(rnorm(N*M), nrow = N, ncol = M)
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#Iterate through each value in the s-vector representing number of sparse element in vector x
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for (s in s_vector) {
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x <- numeric(512)
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ind <- sample(1:512, s) # select s random locations which will have non zero values
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for (i in ind){
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x[i] <- x_full[i]
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}
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#x <- rsparsematrix(512, 1, nnz = s, rand.x=rnorm)
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y <- A%*%x
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x0 <- t(A)%*%y
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xp <- l1eq_pd(x0,A,y)
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print(paste0(s,"/128 ","LHS: ",norm(xp-x,type='2'),"<=","RHS: ",10^-4*norm(x,type='2')," ",norm(xp-x,type='2') <= 10^-4*norm(x,type='2')))
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#10^-4*
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if (norm(xp-x,type='2') <= norm(x,type='2')) {
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error_vector[s]=1
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}
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else{
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error_vector[s]=0
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}
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}
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error_matrix[i,]=error_vector
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}
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plot(colMeans(error_matrix),type='l')
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s_vector <- seq(1,128,1)
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source('l1eq_pd.R')
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num_experiments <- 4
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error_matrix <- matrix(0, num_experiments, 128)
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M <- 512
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N <- 256
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x_matrix <- matrix(0, 128, 128)
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error_vector <- rep(0,128)
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x_num <- seq(1,num_experiments,1)
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#Iterate through each experiement created
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for (i in x_num) {
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print(paste0("Running experiment number ",i))
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x_full <- rsparsematrix(512, 1, nnz = 512, rand.x=rnorm)
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#Create A matrix consisting of guassian distribution of i.i.d variables 256 (rows) X 512 (columns)
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A <- matrix(rnorm(N*M), nrow = N, ncol = M)
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#Iterate through each value in the s-vector representing number of sparse element in vector x
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for (s in s_vector) {
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x <- numeric(512)
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ind <- sample(1:512, s) # select s random locations which will have non zero values
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for (i in ind){
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x[i] <- x_full[i]
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}
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#x <- rsparsematrix(512, 1, nnz = s, rand.x=rnorm)
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y <- A%*%x
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x0 <- t(A)%*%y
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xp <- l1eq_pd(x0,A,y)
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print(paste0(s,"/128 ","LHS: ",norm(xp-x,type='2'),"<=","RHS: ",10^-4*norm(x,type='2')," ",norm(xp-x,type='2') <= 10^-4*norm(x,type='2')))
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#10^-4*
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if (norm(xp-x,type='2') <= 10^-4*norm(x,type='2')) {
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error_vector[s]=1
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}
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else{
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error_vector[s]=0
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}
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}
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error_matrix[i,]=error_vector
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}
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plot(colMeans(error_matrix),type='l')
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s_vector <- seq(1,128,1)
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source('l1eq_pd.R')
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num_experiments <- 4
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error_matrix <- matrix(0, num_experiments, 128)
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M <- 512
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N <- 256
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x_matrix <- matrix(0, 128, 128)
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error_vector <- rep(0,128)
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x_num <- seq(1,num_experiments,1)
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#Iterate through each experiement created
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for (i in x_num) {
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print(paste0("Running experiment number ",i))
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x_full <- rsparsematrix(512, 1, nnz = 512, rand.x=rnorm)
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#Create A matrix consisting of guassian distribution of i.i.d variables 256 (rows) X 512 (columns)
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A <- matrix(rnorm(N*M), nrow = N, ncol = M)
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#Iterate through each value in the s-vector representing number of sparse element in vector x
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for (s in s_vector) {
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x <- numeric(512)
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ind <- sample(1:512, s) # select s random locations which will have non zero values
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for (i in ind){
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x[i] <- x_full[i]
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}
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#x <- rsparsematrix(512, 1, nnz = s, rand.x=rnorm)
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y <- A%*%x
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x0 <- t(A)%*%y
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xp <- l1eq_pd(x0,A,y)
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print(paste0(s,"/128 ","LHS: ",norm(xp-x,type='2'),"<=","RHS: ",10^-4*norm(x,type='2')," ",norm(xp-x,type='2') <= 10^-4*norm(x,type='2')))
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#10^-4*
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if (norm(xp-x,type='2') <= 10^-4*norm(x,type='2')) {
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error_vector[s]=1
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}
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else{
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error_vector[s]=0
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}
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}
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error_matrix[i,]=error_vector
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}
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error_matrix[i,]=error_vector
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View(error_matrix)
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View(error_matrix)
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error_vector
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error_matrix[,i]=error_vector
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error_matrix[i,]
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error_matrix[1,]
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i
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s_vector <- seq(1,128,1)
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source('l1eq_pd.R')
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num_experiments <- 4
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error_matrix <- matrix(0, num_experiments, 128)
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M <- 512
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N <- 256
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x_matrix <- matrix(0, 128, 128)
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error_vector <- rep(0,128)
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x_num <- seq(1,num_experiments,1)
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#Iterate through each experiement created
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for (num_x in x_num) {
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print(paste0("Running experiment number ",i))
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x_full <- rsparsematrix(512, 1, nnz = 512, rand.x=rnorm)
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#Create A matrix consisting of guassian distribution of i.i.d variables 256 (rows) X 512 (columns)
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A <- matrix(rnorm(N*M), nrow = N, ncol = M)
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#Iterate through each value in the s-vector representing number of sparse element in vector x
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for (s in s_vector) {
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x <- numeric(512)
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ind <- sample(1:512, s) # select s random locations which will have non zero values
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for (i in ind){
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x[i] <- x_full[i]
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}
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#x <- rsparsematrix(512, 1, nnz = s, rand.x=rnorm)
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y <- A%*%x
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x0 <- t(A)%*%y
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xp <- l1eq_pd(x0,A,y)
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print(paste0(s,"/128 ","LHS: ",norm(xp-x,type='2'),"<=","RHS: ",10^-4*norm(x,type='2')," ",norm(xp-x,type='2') <= 10^-4*norm(x,type='2')))
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#10^-4*
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if (norm(xp-x,type='2') <= 10^-4*norm(x,type='2')) {
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error_vector[s]=1
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}
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else{
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error_vector[s]=0
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}
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|
}
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error_matrix[,num_x]=error_vector
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}
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error_matrix[num_x,]=error_vector
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s_vector <- seq(1,128,1)
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source('l1eq_pd.R')
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num_experiments <- 10
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error_matrix <- matrix(0, num_experiments, 128)
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M <- 512
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N <- 256
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x_matrix <- matrix(0, 128, 128)
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error_vector <- rep(0,128)
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x_num <- seq(1,num_experiments,1)
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#Iterate through each experiement created
|
|
for (num_x in x_num) {
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print(paste0("Running experiment number ",i))
|
|
x_full <- rsparsematrix(512, 1, nnz = 512, rand.x=rnorm)
|
|
#Create A matrix consisting of guassian distribution of i.i.d variables 256 (rows) X 512 (columns)
|
|
A <- matrix(rnorm(N*M), nrow = N, ncol = M)
|
|
#Iterate through each value in the s-vector representing number of sparse element in vector x
|
|
for (s in s_vector) {
|
|
x <- numeric(512)
|
|
ind <- sample(1:512, s) # select s random locations which will have non zero values
|
|
for (i in ind){
|
|
x[i] <- x_full[i]
|
|
}
|
|
#x <- rsparsematrix(512, 1, nnz = s, rand.x=rnorm)
|
|
y <- A%*%x
|
|
x0 <- t(A)%*%y
|
|
xp <- l1eq_pd(x0,A,y)
|
|
print(paste0(s,"/128 ","LHS: ",norm(xp-x,type='2'),"<=","RHS: ",10^-4*norm(x,type='2')," ",norm(xp-x,type='2') <= 10^-4*norm(x,type='2')))
|
|
#10^-4*
|
|
if (norm(xp-x,type='2') <= 10^-4*norm(x,type='2')) {
|
|
error_vector[s]=1
|
|
}
|
|
else{
|
|
error_vector[s]=0
|
|
}
|
|
}
|
|
error_matrix[num_x,]=error_vector
|
|
}
|
|
s_vector <- seq(1,128,1)
|
|
source('l1eq_pd.R')
|
|
num_experiments <- 10
|
|
error_matrix <- matrix(0, num_experiments, 128)
|
|
M <- 512
|
|
N <- 256
|
|
x_matrix <- matrix(0, 128, 128)
|
|
error_vector <- rep(0,128)
|
|
x_num <- seq(1,num_experiments,1)
|
|
#Iterate through each experiement created
|
|
for (num_x in x_num) {
|
|
print(paste0("Running experiment number ",num_x))
|
|
x_full <- rsparsematrix(512, 1, nnz = 512, rand.x=rnorm)
|
|
#Create A matrix consisting of guassian distribution of i.i.d variables 256 (rows) X 512 (columns)
|
|
A <- matrix(rnorm(N*M), nrow = N, ncol = M)
|
|
#Iterate through each value in the s-vector representing number of sparse element in vector x
|
|
for (s in s_vector) {
|
|
x <- numeric(512)
|
|
ind <- sample(1:512, s) # select s random locations which will have non zero values
|
|
for (i in ind){
|
|
x[i] <- x_full[i]
|
|
}
|
|
#x <- rsparsematrix(512, 1, nnz = s, rand.x=rnorm)
|
|
y <- A%*%x
|
|
x0 <- t(A)%*%y
|
|
xp <- l1eq_pd(x0,A,y)
|
|
print(paste0(s,"/128 ","LHS: ",norm(xp-x,type='2'),"<=","RHS: ",10^-4*norm(x,type='2')," ",norm(xp-x,type='2') <= 10^-4*norm(x,type='2')))
|
|
#10^-4*
|
|
if (norm(xp-x,type='2') <= 10^-4*norm(x,type='2')) {
|
|
error_vector[s]=1
|
|
}
|
|
else{
|
|
error_vector[s]=0
|
|
}
|
|
}
|
|
error_matrix[num_x,]=error_vector
|
|
}
|
|
plot(colMeans(error_matrix),type='l')
|
|
plot(colMeans(error_matrix),type='l',xlab='Sparsity',ylab='Average Success Rate')
|
|
plot(colMeans(error_matrix),type='l',xlab='Sparsity',ylab='Success Rate')
|