addition of a multivariate example

Author: Vandenplas, Jeremie

CMakeLists.txt

@@ -78,7 +78,7 @@ foreach(execid mnist network_save network_sync)
   add_test(test_${execid} bin/test_${execid})
 endforeach()
 
-foreach(execid mnist montesinos_uni simple sine)
+foreach(execid mnist montesinos_uni montesinos_multi simple sine)
   add_executable(example_${execid} src/tests/example_${execid}.f90)
   target_link_libraries(example_${execid} neural ${LIBS})
   add_test(example_${execid} bin/example_${execid})

README.md

@@ -309,25 +309,44 @@ for example on 16 cores using [OpenCoarrays](https://github.com/sourceryinstitut
 $ cafrun -n 16 ./example_mnist
 ```
 
-### Montesinos-Lopez et al. (2018) example
+### Montesinos-Lopez et al. (2018) univariate example
 
-The Montesinos-Lopez et al. (2018) example is extracted from the study:
+The Montesinos-Lopez et al. (2018) univariate example is extracted from the study:
 
 ```
 Montesinos-Lopez et al. 2018. Multi-environment genomic prediction of plant traits using deep learners with dense architecture. G3, 8, 3813-3828.
 ```
 
-This example uses the data from the dataset "Data\_Maize\_1to3", and was extracted using the R code in the Appendix.
+This example uses the data from the dataset "Data\_Maize\_1to3", and was extracted using the R code in the Appendix of this paper.
 
 
-The Montesinos-Lopez data is included with the repo and you will have to unpack it first:
+The Montesinos-Lopez univariate data is included with the repo and you will have to unpack it first:
 
 ```
 cd data/montesinos_uni
 tar xzvf montesinos_uni.tar.gz
 cd -
 ```
 
+### Montesinos-Lopez et al. (2018) multivariate example
+
+The Montesinos-Lopez et al. (2018) multivariate example is extracted from the study:
+
+```
+Montesinos-Lopez et al. 2018. Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits. G3, 8, 3829-3840.
+```
+
+This example uses the data from the dataset "Data\_Maize\_set\_1", and was extracted using the R code in the Appendix B of this paper.
+
+
+The Montesinos-Lopez multivariate data is included with the repo and you will have to unpack it first:
+
+```
+cd data/montesinos_multi
+tar xzvf montesinos_multi.tar.gz
+cd -
+```
+
 
 ## Contributing
 

data/montesinos_multi.tar.gz

@@ -20,7 +20,7 @@ program example_mnist
 
   call load_mnist(tr_images, tr_labels, te_images, te_labels)
 
-  net = network_type([784, 10, 10])
+  net = network_type([size(tr_images,dim=1), 10, size(label_digits(te_labels),dim=1)])
 
   batch_size = 1000
   num_epochs = 10

src/tests/example_mnist.f90

@@ -0,0 +1,136 @@
+program example_montesinos_multi
+ use mod_kinds,only:ik,rk
+ use mod_network,only:network_type
+ implicit none
+ integer(ik)::ny1_tr,ny2_tr,nx1_tr,nx2_tr
+ integer(ik)::ny1_ts,ny2_ts,nx1_ts,nx2_ts
+
+ integer(ik)::batch_size,num_epochs
+
+ real(rk),allocatable::y_tr(:,:),x_tr(:,:)
+ real(rk),allocatable::y_ts(:,:),x_ts(:,:)
+
+ type(network_type)::net
+
+ call readfile('../data/montesinos_multi/y_tr.dat',ny1_tr,ny2_tr,y_tr)
+ call readfile('../data/montesinos_multi/x_tr.dat',nx1_tr,nx2_tr,x_tr)
+ 
+ net=network_type([nx1_tr,50,50,ny1_tr])
+
+ batch_size=50
+ num_epochs=50
+
+ !training
+ call net%fit(x_tr,y_tr,3._rk,epochs=num_epochs,batch_size=batch_size)
+
+ call net%sync(1)
+
+ !validation
+ call readfile('../data/montesinos_multi/y_ts.dat',ny1_ts,ny2_ts,y_ts)
+ call readfile('../data/montesinos_multi/x_ts.dat',nx1_ts,nx2_ts,x_ts)
+
+ if(this_image().eq.1)then
+  write(*,*)'Correlation(s): ',corr_array(net%predict(x_ts),y_ts)
+ endif
+
+contains
+
+subroutine readfile(filename,n,m,array)
+ character(len=*),intent(in)::filename
+ integer(ik),intent(out)::n,m
+ real(rk),allocatable,intent(out)::array(:,:)
+
+ integer(ik)::un,i,io
+
+ open(newunit=un,file=filename,status='old',action='read')
+ call numlines(un,m)
+ call numcol(un,n) 
+
+ allocate(array(n,m))
+ rewind(un)
+ do i=1,m
+  read(un,*,iostat=io)array(:,i)
+  if(io.ne.0)exit
+ enddo
+ close(un)
+ 
+end subroutine
+
+pure function corr_array(array1,array2) result(a)
+ real(rk),intent(in)::array1(:,:),array2(:,:)
+ real(rk),allocatable::a(:)
+ 
+ integer(ik)::i,n
+ 
+ n=size(array1,dim=1)
+ 
+ allocate(a(n))
+ a=0.0_rk
+ do i=1,n
+  a(i)=corr(array1(i,:),array2(i,:))
+ enddo
+
+end function
+
+pure real(rk) function corr(array1,array2)
+ real(rk),intent(in)::array1(:),array2(:)
+ 
+ real(rk)::mean1,mean2
+
+ !brute force
+ 
+ mean1=sum(array1)/size(array1)
+ mean2=sum(array2)/size(array2)
+ corr=dot_product(array1-mean1,array2-mean2)/sqrt(sum((array1-mean1)**2)*sum((array2-mean2)**2))
+
+end function
+
+subroutine numlines(unfile,n)
+ implicit none
+ integer::io
+ integer,intent(in)::unfile
+ integer,intent(out)::n
+ rewind(unfile)
+ n=0
+ do
+  read(unfile,*,iostat=io)
+  if (io.ne.0) exit
+  n=n+1
+ enddo
+ rewind(unfile)
+end subroutine
+
+subroutine numcol(unfile,n)
+ implicit none
+ integer,intent(in)::unfile
+ character(len=1000000)::a
+ integer,intent(out)::n
+ integer::curr,first,last,lena,stat,i
+ rewind(unfile)
+ read(unfile,"(a)")a
+ curr=1;lena=len(a);n=0
+ do
+  first=0
+  do i=curr,lena
+   if (a(i:i) /= " ") then
+    first=i
+    exit
+   endif
+  enddo
+  if (first == 0) exit
+  curr=first+1
+  last=0
+  do i=curr,lena
+   if (a(i:i) == " ") then
+    last=i
+    exit
+   endif
+  enddo
+  if (last == 0) last=lena
+  n=n+1
+  curr=last+1
+ enddo
+ rewind(unfile)
+end subroutine
+
+end program