--- /dev/null
+/********************************************************/
+/* ____ _ _____ __ __ ____ _ _ _____ */
+/* | _ \(_)___| ____| \/ | __ )| | / | |___ / */
+/* | | | | / __| _| | |\/| | _ \| | | | |_ \ */
+/* | |_| | \__ \ |___| | | | |_) | |___ | |_ ___) | */
+/* |____/|_|___/_____|_| |_|____/|_____| |_(_)____/ */
+/* DisEMBL is Copyright (C) 2003 */
+/* Lars Juhl Jensen & Rune Linding - EMBL */
+/* Licensed under GPL */
+/********************************************************/
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+/* Define size of the alphabet */
+#define na 21
+
+/* Define max number of neurons */
+#define mw 41
+#define mh 30
+
+#include "russel.h"
+#include "bfactor.h"
+#include "missing.h"
+
+float sigmoid[256] = {
+ 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+ 0.000000, 0.000000, 0.000000, 0.000000, 0.000001, 0.000001, 0.000001, 0.000001,
+ 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000002, 0.000002, 0.000002,
+ 0.000002, 0.000003, 0.000003, 0.000003, 0.000004, 0.000004, 0.000005, 0.000005,
+ 0.000006, 0.000007, 0.000008, 0.000009, 0.000010, 0.000011, 0.000013, 0.000015,
+ 0.000017, 0.000019, 0.000021, 0.000024, 0.000028, 0.000031, 0.000035, 0.000040,
+ 0.000045, 0.000051, 0.000058, 0.000066, 0.000075, 0.000085, 0.000096, 0.000109,
+ 0.000123, 0.000140, 0.000158, 0.000180, 0.000203, 0.000231, 0.000261, 0.000296,
+ 0.000335, 0.000380, 0.000431, 0.000488, 0.000553, 0.000626, 0.000710, 0.000804,
+ 0.000911, 0.001032, 0.001170, 0.001325, 0.001501, 0.001701, 0.001927, 0.002183,
+ 0.002473, 0.002801, 0.003173, 0.003594, 0.004070, 0.004610, 0.005220, 0.005911,
+ 0.006693, 0.007577, 0.008577, 0.009708, 0.010987, 0.012432, 0.014064, 0.015906,
+ 0.017986, 0.020332, 0.022977, 0.025957, 0.029312, 0.033086, 0.037327, 0.042088,
+ 0.047426, 0.053403, 0.060087, 0.067547, 0.075858, 0.085099, 0.095349, 0.106691,
+ 0.119203, 0.132964, 0.148047, 0.164516, 0.182426, 0.201813, 0.222700, 0.245085,
+ 0.268941, 0.294215, 0.320821, 0.348645, 0.377541, 0.407333, 0.437823, 0.468791,
+ 0.500000, 0.531209, 0.562177, 0.592667, 0.622459, 0.651355, 0.679179, 0.705785,
+ 0.731059, 0.754915, 0.777300, 0.798187, 0.817574, 0.835484, 0.851953, 0.867036,
+ 0.880797, 0.893309, 0.904651, 0.914901, 0.924142, 0.932453, 0.939913, 0.946597,
+ 0.952574, 0.957912, 0.962673, 0.966914, 0.970688, 0.974043, 0.977023, 0.979668,
+ 0.982014, 0.984094, 0.985936, 0.987568, 0.989013, 0.990292, 0.991423, 0.992423,
+ 0.993307, 0.994089, 0.994780, 0.995390, 0.995930, 0.996406, 0.996827, 0.997199,
+ 0.997527, 0.997817, 0.998073, 0.998299, 0.998499, 0.998675, 0.998830, 0.998968,
+ 0.999089, 0.999196, 0.999290, 0.999374, 0.999447, 0.999512, 0.999569, 0.999620,
+ 0.999665, 0.999704, 0.999739, 0.999769, 0.999797, 0.999820, 0.999842, 0.999860,
+ 0.999877, 0.999891, 0.999904, 0.999915, 0.999925, 0.999934, 0.999942, 0.999949,
+ 0.999955, 0.999960, 0.999965, 0.999969, 0.999972, 0.999976, 0.999979, 0.999981,
+ 0.999983, 0.999985, 0.999987, 0.999989, 0.999990, 0.999991, 0.999992, 0.999993,
+ 0.999994, 0.999995, 0.999995, 0.999996, 0.999996, 0.999997, 0.999997, 0.999997,
+ 0.999998, 0.999998, 0.999998, 0.999998, 0.999999, 0.999999, 0.999999, 0.999999,
+ 0.999999, 0.999999, 0.999999, 0.999999, 0.999999, 1.000000, 1.000000, 1.000000,
+ 1.000000, 1.000000, 1.000000, 1.000000, 1.000000, 1.000000, 1.000000, 1.000000
+};
+
+
+
+
+/*
+ * Perform feed forward evaluation of a neural network on a sequence window.
+ */
+
+float feed_forward(int const *s, float const w[], int nw, int nh) {
+
+ float h[mh], o[2], x;
+ int i, j;
+
+ /* Shift input window to match network window size */
+ s += (mw-nw)/2;
+
+ /* Feed input values to hidden layer making use of sparse encoding */
+ for (i = 0; i < nh; ++i) {
+ x = w[(na*nw+1)*(i+1)-1];
+ for (j = 0; j < nw; ++j) {
+ x += w[(na*nw+1)*i+na*j+s[j]];
+ }
+ if (x <= -16) {
+ h[i] = 0;
+ } else if (x >= 16) {
+ h[i] = 1;
+ } else {
+ h[i] = sigmoid[(int)(8*x+128)];
+ }
+ }
+
+ /* Feed hidden layer values to output layer */
+ for (i = 0; i <= 1; ++i) {
+ x = w[(na*nw+1)*nh+(nh+1)*(i+1)-1];
+ for (j = 0; j < nh; ++j) {
+ x += w[(na*nw+1)*nh+(nh+1)*i+j]*h[j];
+ }
+ if (x <= -16) {
+ o[i] = 0;
+ } else if (x >= 16) {
+ o[i] = 1;
+ } else {
+ o[i] = sigmoid[(int)(8*x+128)];
+ }
+ }
+
+ /* Combine the scores from the two output neurons */
+ return((o[0]+1-o[1])/2);
+
+}
+
+
+
+
+/*
+ * Calculate and print scores for a sequence window.
+ */
+
+void predict(int const *s) {
+
+ float sm, sb, sr;
+
+ if (s[(mw-1)/2] == na-1) {
+ return;
+ }
+
+ sr = 0;
+ sr += feed_forward(s, r19_1, 19, 30);
+ sr += feed_forward(s, r19_2, 19, 30);
+ sr += feed_forward(s, r19_3, 19, 30);
+ sr += feed_forward(s, r19_4, 19, 30);
+ sr += feed_forward(s, r19_5, 19, 30);
+ sr /= 5;
+ sr = 0.07387214+0.8020778*sr;
+
+ sb = 0;
+ sb += feed_forward(s, b41_1, 41, 5);
+ sb += feed_forward(s, b41_2, 41, 5);
+ sb += feed_forward(s, b41_3, 41, 5);
+ sb += feed_forward(s, b41_4, 41, 5);
+ sb += feed_forward(s, b41_5, 41, 5);
+ sb /= 5;
+ sb = 0.08016882+0.6282424*sb;
+
+ sm = 0;
+ sm += feed_forward(s, m9_1, 9, 30);
+ sm += feed_forward(s, m9_2, 9, 30);
+ sm += feed_forward(s, m9_3, 9, 30);
+ sm += feed_forward(s, m9_4, 9, 30);
+ sm += feed_forward(s, m9_5, 9, 30);
+ sm += feed_forward(s, m21_1, 21, 30);
+ sm += feed_forward(s, m21_2, 21, 30);
+ sm += feed_forward(s, m21_3, 21, 30);
+ sm += feed_forward(s, m21_4, 21, 30);
+ sm += feed_forward(s, m21_5, 21, 30);
+ sm /= 10;
+
+ printf("%f\t%f\t%f\n", sr, sr*sb, sm);
+
+}
+
+int main(int ARGC, char *ARGV[]) {
+
+ char *alphabet = strdup("FIVWMLCHYAGNRTPDEQSK");
+
+ char *p;
+ int c, i, j, s[mw];
+
+ for (i = 0; i < mw; ++i) {
+ s[i] = na-1;
+ }
+
+ while (!feof(stdin)) {
+
+ c = fgetc(stdin);
+ p = strchr(alphabet, c);
+ if (p != NULL) {
+ for (i = 1; i < mw; ++i) {
+ s[mw-i] = s[mw-i-1];
+ }
+ s[0] = p-alphabet;
+ predict(s);
+ } else if ((char)c == '\n') {
+ for (i = 0; i < (mw-1)/2; i++) {
+ for (j = 1; j < mw; ++j) {
+ s[mw-j] = s[mw-j-1];
+ }
+ s[0] = na-1;
+ predict(s);
+ }
+ }
+
+ }
+
+ return(0);
+
+}
git://source.jalview.org / jabaws.git / blobdiff