From a753eca0121ac3ec81ed76bd719d3f1cb9522680 Mon Sep 17 00:00:00 2001
From: Determinant <ted.sybil@gmail.com>
Date: Wed, 3 Jun 2015 23:00:30 +0800
Subject: ...

---
 tools/tnet_to_nerv.c   | 57 ++++++++++++++++++++++++++++++++++++++++++
 tools/tnet_to_nerv.cpp | 68 ++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 125 insertions(+)
 create mode 100644 tools/tnet_to_nerv.c
 create mode 100644 tools/tnet_to_nerv.cpp

(limited to 'tools')

diff --git a/tools/tnet_to_nerv.c b/tools/tnet_to_nerv.c
new file mode 100644
index 0000000..f781236
--- /dev/null
+++ b/tools/tnet_to_nerv.c
@@ -0,0 +1,57 @@
+#include <stdio.h>
+#include <string.h>
+#include <stdlib.h>
+char token[1024];
+double mat[4096][4096];
+int main() {
+    FILE *fout = fopen("converted.nerv", "w");
+    int cnt = 0;
+    while (scanf("%s", token) != EOF)
+    {
+        int nrow, ncol;
+        int i, j;
+        if (strcmp(token, "<biasedlinearity>") == 0)
+        {
+            scanf("%d %d", &ncol, &nrow);
+            scanf("%s %d %d", token, &ncol, &nrow);
+            printf("%d %d\n", nrow, ncol);
+            for (j = 0; j < ncol; j++)
+                for (i = 0; i < nrow; i++)
+                    scanf("%lf", mat[i] + j);
+            off_t base = ftello(fout);
+            fprintf(fout, "%16d", 0);
+            fprintf(fout, "{type=\"nerv.LinearTransParam\",id=\"affine%d_ltp\"}\n",
+                            cnt);
+            fprintf(fout, "%d %d\n", nrow, ncol);
+            for (i = 0; i < nrow; i++)
+            {
+                for (j = 0; j < ncol; j++)
+                    fprintf(fout, "%.8f ", mat[i][j]);
+                fprintf(fout, "\n");
+            }
+            size_t length = ftello(fout) - base;
+            fseeko(fout, base, SEEK_SET);
+            fprintf(fout, "[%13lu]\n", length);
+            fseeko(fout, 0, SEEK_END);
+            if (scanf("%s %d", token, &ncol) == 2 && *token == 'v')
+            {
+                base = ftello(fout);
+                for (j = 0; j < ncol; j++)
+                    scanf("%lf", mat[0] + j);
+                fprintf(fout, "%16d", 0);
+                fprintf(fout, "{type=\"nerv.BiasParam\",id=\"affine%d_bp\"}\n",
+                        cnt);
+                fprintf(fout, "1 %d\n", nrow, ncol);
+                for (j = 0; j < ncol; j++)
+                    fprintf(fout, "%.8f ", mat[0][j]);
+                fprintf(fout, "\n");
+                length = ftello(fout) - base;
+                fseeko(fout, base, SEEK_SET);
+                fprintf(fout, "[%13lu]\n", length);
+                cnt++;
+                fseeko(fout, 0, SEEK_END);
+            }
+        }
+    }
+    return 0;
+}
diff --git a/tools/tnet_to_nerv.cpp b/tools/tnet_to_nerv.cpp
new file mode 100644
index 0000000..cedf27a
--- /dev/null
+++ b/tools/tnet_to_nerv.cpp
@@ -0,0 +1,68 @@
+#include <cstdio>
+#include <fstream>
+#include <string>
+#include <cstring>
+char token[1024];
+char output[1024];
+double mat[4096][4096];
+int main() {
+    std::ofstream fout;
+    fout.open("converted.nerv");
+    int cnt = 0;
+    while (scanf("%s", token) != EOF)
+    {
+        int nrow, ncol;
+        int i, j;
+        if (strcmp(token, "<biasedlinearity>") == 0)
+        {
+            scanf("%d %d", &ncol, &nrow);
+            scanf("%s %d %d", token, &ncol, &nrow);
+            printf("%d %d\n", nrow, ncol);
+            for (j = 0; j < ncol; j++)
+                for (i = 0; i < nrow; i++)
+                    scanf("%lf", mat[i] + j);
+            long base = fout.tellp();
+            sprintf(output, "%16d", 0);
+            fout << output;
+            sprintf(output, "{type=\"nerv.LinearTransParam\",id=\"affine%d_ltp\"}\n",
+                            cnt);
+            fout << output;
+            sprintf(output, "%d %d\n", nrow, ncol);
+            fout << output;
+            for (i = 0; i < nrow; i++)
+            {
+                for (j = 0; j < ncol; j++)
+                    fout << mat[i][j] << " ";
+                fout << std::endl;
+            }
+            long length = fout.tellp() - base;
+            fout.seekp(base);
+            sprintf(output, "[%13lu]\n", length);
+            fout << output;
+            fout.seekp(0, std::ios_base::end);
+            if (scanf("%s %d", token, &ncol) == 2 && *token == 'v')
+            {
+                base = fout.tellp();
+                for (j = 0; j < ncol; j++)
+                    scanf("%lf", mat[0] + j);
+                sprintf(output, "%16d", 0);
+                fout << output;
+                sprintf(output, "{type=\"nerv.BiasParam\",id=\"affine%d_bp\"}\n",
+                        cnt);
+                fout << output;
+                sprintf(output, "1 %d\n", ncol);
+                fout << output;
+                for (j = 0; j < ncol; j++)
+                    fout << mat[0][j] << " ";
+                fout << std::endl;
+                length = fout.tellp() - base;
+                fout.seekp(base);
+                sprintf(output, "[%13lu]\n", length);
+                fout << output;
+                fout.seekp(0, std::ios_base::end);
+                cnt++;
+            }
+        }
+    }
+    return 0;
+}
-- 
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