This is the Perceptron algorithm, I wrote this implementation with my friend. It gets the job done, but it's quite dirty, perhaps one of you stylish hackers might help me beautify this beast.
This is what I made to generate my data structures and run everything:
public class FileDictCreateur
{
static String PATH = "/home/yamada/Workbench/SUTD/ISTD_50.570/assignments/practice_data/data/train";
//the global list of all words across all articles
static Set<String> GLOBO_DICT = new HashSet<String>();
//is the globo dict full?
static boolean globo_dict_fixed = false;
// hash map of all the words contained in individual files
static Map<File, ArrayList<String> > training_fileDict = new HashMap<>();
static Map<File, ArrayList<String> > test_fileDict = new HashMap<>();
//input to perceptron. final struc.
static Map<File, int[] > training_perceptron_input = new HashMap<>();
static Map<File, int[] > test_perceptron_input = new HashMap<>();
public static void main(String[] args) throws IOException
{
//each of the diferent categories
String[] categories = { "/atheism", "/politics", "/science", "/sports"};
//cycle through all categories once to populate the global dict
for(int cycle = 0; cycle <= 3; cycle++)
{
String general_data_partition = PATH + categories[cycle];
File directory = new File( general_data_partition );
iterateDirectory( directory , globo_dict_fixed, training_fileDict ); //that training dict doesn't need to be here
// ^ NEEDS REFACTORING!!!
if(cycle == 3)
globo_dict_fixed = true;
}
//cycle through again to populate the file dicts
for(int cycle = 0; cycle <= 3; cycle++)
{
String general_data_partition = PATH + categories[cycle];
File directory = new File( general_data_partition );
iterateDirectory( directory , globo_dict_fixed, training_fileDict );
}
//get feature vectors for the training data
perceptron_data_struc_generateur( GLOBO_DICT, training_fileDict, training_perceptron_input );
//cycle through all categories once to populate the global dict
for(int cycle = 0; cycle <= 3; cycle++)
{
//get the test data
String test_path = "/home/yamada/Workbench/SUTD/ISTD_50.570/assignments/practice_data/data/test";
File test_dict = new File( test_path + categories[cycle]);
iterateDirectory( test_dict , globo_dict_fixed, test_fileDict );
}
//get feature vectors for the TEST data
perceptron_data_struc_generateur( GLOBO_DICT, test_fileDict, test_perceptron_input );
for (Map.Entry<File, int[]> entry : test_perceptron_input.entrySet())
{
System.out.println(entry.getKey() + ", " + Arrays.toString(entry.getValue()));
}
///AHHAHAHAHH!!!!!!
Perceptron.perceptron( GLOBO_DICT, training_perceptron_input, test_perceptron_input );
}
private static void iterateDirectory(File directory, boolean globo_dict_fixed, Map<File, ArrayList<String> > fileDict ) throws IOException
{
for (File file : directory.listFiles())
{
if (file.isDirectory())
{
iterateDirectory(directory, globo_dict_fixed, fileDict );
}
else
{
String line;
BufferedReader br = new BufferedReader(new FileReader( file ));
while ((line = br.readLine()) != null)
{
String[] words = line.split(" ");//those are your words
if(globo_dict_fixed == false)
{
populate_globo_dict( words );
}
else
{
create_file_dict( file, words, fileDict );
}
}
}
}
}
@SuppressWarnings("unchecked")
public static void create_file_dict( File file, String[] words, Map<File, ArrayList<String> > fileDict ) throws IOException
{
if (!fileDict.containsKey(file))
{
@SuppressWarnings("rawtypes")
ArrayList document_words = new ArrayList<String>();
String word;
for (int i = 0; i < words.length; i++)
{
word = words[i];
document_words.add(word);
}
fileDict.put(file, document_words);
}
}
public static void populate_globo_dict( String[] words ) throws IOException
{
String word;
for (int i = 0; i < words.length; i++)
{
word = words[i];
if (!GLOBO_DICT.contains(word))
{
GLOBO_DICT.add(word);
}
}
}
public static void perceptron_data_struc_generateur(Set<String> GLOBO_DICT,
Map<File, ArrayList<String> > fileDict,
Map<File, int[] > perceptron_input)
{
//create a new entry in the array list 'perceptron_input'
//with the key as the file name from fileDict
//create a new array which is the length of GLOBO_DICT
//iterate through the indicies of GLOBO_DICT
//for all words in globo dict, if that word appears in fileDict,
//increment the perceptron_input index that corresponds to that
//word in GLOBO_DICT by the number of times that word appears in fileDict
//so i can get the index later
List<String> GLOBO_DICT_list = new ArrayList<>(GLOBO_DICT);
for (Map.Entry<File, ArrayList<String>> entry : fileDict.entrySet())
{
int[] cross_czech = new int[GLOBO_DICT_list.size()];
//initialize to zero
Arrays.fill(cross_czech, 0);
for (String s : GLOBO_DICT_list)
{
for(String st : entry.getValue())
{
if( st.equals(s) )
{
cross_czech[ GLOBO_DICT_list.indexOf( s ) ] = cross_czech[ GLOBO_DICT_list.indexOf( s ) ] +1;
}
}
}
perceptron_input.put( entry.getKey() , cross_czech);
}
}
}
I was having a lot of trouble trying to implement the perceptron algorithm, but then I found this tutorial, it goes through a java implementation and then has some example code. I substituted my own data structures for there ones in the tutorial, and it works! :)
HOWEVER
I made this substitution in the most simplistic possible way, manually enumerating my data structures. This works as a proof of concept, for my experimental "toy" data, but most certainly is not able to tackle the real data I want to consider. It's far too rigid.
Perhaps someone more proficient in abstract thinking and loops would be able to show me how I can improve this code.
The important data structure to consider is Map<File, int[] >, it looks like this:
/data/test/sports/t.s_1.txt, [0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0]
/data/test/politics/t.p_0.txt, [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
/data/test/atheism/t.a_0.txt, [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
/data/test/science/t.s_0.txt, [1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0]
The code above needs to be generalized. You can also find the full programme on GitHub.
public static void perceptron( Set<String> GLOBO_DICT, Map<File, int[] > training_perceptron_input, Map<File, int[] > test_perceptron_input)
{
//number of features, number of x, y, z
int size_of_globo_dict = GLOBO_DICT.size();
//number of instances
int NUM_INSTANCES = training_perceptron_input.size();
//three variables (features) they enumerate by
//features, xyz, i also do that
double[] a00 = new double [NUM_INSTANCES];
double[] a01 = new double [NUM_INSTANCES];
double[] a02 = new double [NUM_INSTANCES];
double[] a03 = new double [NUM_INSTANCES];
double[] a04 = new double [NUM_INSTANCES];
double[] a05 = new double [NUM_INSTANCES];
double[] a06 = new double [NUM_INSTANCES];
double[] a07 = new double [NUM_INSTANCES];
double[] a08 = new double [NUM_INSTANCES];
double[] a09 = new double [NUM_INSTANCES];
double[] a10 = new double [NUM_INSTANCES];
double[] a11 = new double [NUM_INSTANCES];
double[] a12 = new double [NUM_INSTANCES];
double[] a13 = new double [NUM_INSTANCES];
double[] a14 = new double [NUM_INSTANCES];
double[] a15 = new double [NUM_INSTANCES];
double[] a16 = new double [NUM_INSTANCES];
double[] a17 = new double [NUM_INSTANCES];
double[] a18 = new double [NUM_INSTANCES];
double[] a19 = new double [NUM_INSTANCES];
double[] a20 = new double [NUM_INSTANCES];
double[] a21 = new double [NUM_INSTANCES];
double[] a22 = new double [NUM_INSTANCES];
double[] a23 = new double [NUM_INSTANCES];
double[] a24 = new double [NUM_INSTANCES];
double[] a25 = new double [NUM_INSTANCES];
double[] a26 = new double [NUM_INSTANCES];
double[] a27 = new double [NUM_INSTANCES];
double[] a28 = new double [NUM_INSTANCES];
double[] a29 = new double [NUM_INSTANCES];
double[] a30 = new double [NUM_INSTANCES];
//2d array for features
int x = 0;
int[][] feature_matrix = new int[ training_perceptron_input.size() ][ GLOBO_DICT.size() ];
String[][] output_label = new String[ training_perceptron_input.size() ][ 2 ];
for(Entry<File, int[]> entry : training_perceptron_input.entrySet())
{
int[] container =entry.getValue();
for(int j = 0; j < 4; j++)
{
feature_matrix[x][j] = container[j];
output_label[x][1] = entry.getKey().toString();
}
x++;
}
int[] outputs = new int [NUM_INSTANCES];
for(int g = 0; g < NUM_INSTANCES; g++)
{
a00[g] = feature_matrix[g][ 0];
a01[g] = feature_matrix[g][ 1];
a02[g] = feature_matrix[g][ 2];
a03[g] = feature_matrix[g][ 3];
a04[g] = feature_matrix[g][ 4];
a05[g] = feature_matrix[g][ 5];
a06[g] = feature_matrix[g][ 6];
a07[g] = feature_matrix[g][ 7];
a08[g] = feature_matrix[g][ 8];
a09[g] = feature_matrix[g][ 9];
a10[g] = feature_matrix[g][10];
a11[g] = feature_matrix[g][11];
a12[g] = feature_matrix[g][12];
a13[g] = feature_matrix[g][13];
a14[g] = feature_matrix[g][14];
a15[g] = feature_matrix[g][15];
a16[g] = feature_matrix[g][16];
a17[g] = feature_matrix[g][17];
a18[g] = feature_matrix[g][18];
a19[g] = feature_matrix[g][19];
a20[g] = feature_matrix[g][20];
a21[g] = feature_matrix[g][21];
a22[g] = feature_matrix[g][22];
a23[g] = feature_matrix[g][23];
a24[g] = feature_matrix[g][24];
a25[g] = feature_matrix[g][25];
a26[g] = feature_matrix[g][26];
a27[g] = feature_matrix[g][27];
a28[g] = feature_matrix[g][28];
a29[g] = feature_matrix[g][29];
a30[g] = feature_matrix[g][30];
if(output_label[g][1].equals( "/home/yamada/Workbench/SUTD/ISTD_50.570/assignments/practice_data/data/train/atheism/a_0.txt" ) )
{
outputs[g] = 1;
}
else
{
outputs[g] = 0;
}
}
double[] weights = new double[ GLOBO_DICT.size() + 1];// 32 for input variables and one for bias
double localError, globalError;
int i, p, iteration, output;
weights[ 0] = randomNumber(0,1);// w1
weights[ 1] = randomNumber(0,1);// w2
weights[ 2] = randomNumber(0,1);// w3
weights[ 3] = randomNumber(0,1);// w4
weights[ 4] = randomNumber(0,1);// w5
weights[ 5] = randomNumber(0,1);// w6
weights[ 6] = randomNumber(0,1);// w7
weights[ 7] = randomNumber(0,1);// w8
weights[ 8] = randomNumber(0,1);// w9
weights[ 9] = randomNumber(0,1);// w10
weights[10] = randomNumber(0,1);// w11
weights[11] = randomNumber(0,1);// w12
weights[12] = randomNumber(0,1);// w13
weights[13] = randomNumber(0,1);// w14
weights[14] = randomNumber(0,1);// w15
weights[15] = randomNumber(0,1);// w16
weights[16] = randomNumber(0,1);// w17
weights[17] = randomNumber(0,1);// w18
weights[18] = randomNumber(0,1);// w19
weights[19] = randomNumber(0,1);// w20
weights[20] = randomNumber(0,1);// w21
weights[21] = randomNumber(0,1);// w22
weights[22] = randomNumber(0,1);// w23
weights[23] = randomNumber(0,1);// w24
weights[24] = randomNumber(0,1);// w25
weights[25] = randomNumber(0,1);// w26
weights[26] = randomNumber(0,1);// w27
weights[27] = randomNumber(0,1);// w28
weights[28] = randomNumber(0,1);// w29
weights[29] = randomNumber(0,1);// w30
weights[30] = randomNumber(0,1);// w31
weights[31] = randomNumber(0,1);// this is the bias
iteration = 0;
do {
iteration++;
globalError = 0;
//loop through all instances (complete one epoch)
for (p = 0; p < NUM_INSTANCES; p++)
{
// calculate predicted class
output = calculateOutput(theta,
weights,
a00[p],
a01[p],
a02[p],
a03[p],
a04[p],
a05[p],
a06[p],
a07[p],
a08[p],
a09[p],
a10[p],
a11[p],
a12[p],
a13[p],
a14[p],
a15[p],
a16[p],
a17[p],
a18[p],
a19[p],
a20[p],
a21[p],
a22[p],
a23[p],
a24[p],
a25[p],
a26[p],
a27[p],
a28[p],
a29[p],
a30[p]);
// difference between predicted and actual class values
localError = outputs[p] - output;
//update weights and bias
weights[ 0] += LEARNING_RATE * localError * a00[p];
weights[ 1] += LEARNING_RATE * localError * a01[p];
weights[ 2] += LEARNING_RATE * localError * a02[p];
weights[ 3] += LEARNING_RATE * localError * a03[p];
weights[ 4] += LEARNING_RATE * localError * a04[p];
weights[ 5] += LEARNING_RATE * localError * a05[p];
weights[ 6] += LEARNING_RATE * localError * a06[p];
weights[ 7] += LEARNING_RATE * localError * a07[p];
weights[ 8] += LEARNING_RATE * localError * a08[p];
weights[ 9] += LEARNING_RATE * localError * a09[p];
weights[10] += LEARNING_RATE * localError * a10[p];
weights[11] += LEARNING_RATE * localError * a11[p];
weights[12] += LEARNING_RATE * localError * a12[p];
weights[13] += LEARNING_RATE * localError * a13[p];
weights[14] += LEARNING_RATE * localError * a14[p];
weights[15] += LEARNING_RATE * localError * a15[p];
weights[16] += LEARNING_RATE * localError * a16[p];
weights[17] += LEARNING_RATE * localError * a17[p];
weights[18] += LEARNING_RATE * localError * a18[p];
weights[19] += LEARNING_RATE * localError * a19[p];
weights[20] += LEARNING_RATE * localError * a20[p];
weights[21] += LEARNING_RATE * localError * a21[p];
weights[22] += LEARNING_RATE * localError * a22[p];
weights[23] += LEARNING_RATE * localError * a23[p];
weights[24] += LEARNING_RATE * localError * a24[p];
weights[25] += LEARNING_RATE * localError * a25[p];
weights[26] += LEARNING_RATE * localError * a26[p];
weights[27] += LEARNING_RATE * localError * a27[p];
weights[28] += LEARNING_RATE * localError * a28[p];
weights[29] += LEARNING_RATE * localError * a29[p];
weights[30] += LEARNING_RATE * localError * a30[p];
weights[31] += LEARNING_RATE * localError;
//summation of squared error (error value for all instances)
globalError += (localError*localError);
}
/* Root Mean Squared Error */
System.out.println("Iteration "+iteration+" : RMSE = "+Math.sqrt(globalError/NUM_INSTANCES));
}
while (globalError != 0 && iteration <= MAX_ITER);
System.out.println("\n=======\nDecision boundary equation:");
System.out.println(
" a00 *" +
weights[ 0] +
" a01 *" +
weights[ 1] +
" a02 *" +
weights[ 2] +
" a03 *" +
weights[ 3] +
" a04 *" +
weights[ 4] +
" a05 *" +
weights[ 5] +
" a06 *" +
weights[ 6] +
" a07 *" +
weights[ 7] +
" a08 *" +
weights[ 8] +
" a09 *" +
weights[ 9] +
" a10 *" +
weights[10] +
" a11 *" +
weights[11] +
" a12 *" +
weights[12] +
" a13 *" +
weights[13] +
" a14 *" +
weights[14] +
" a15 *" +
weights[15] +
" a16 *" +
weights[16] +
" a17 *" +
weights[17] +
" a18 *" +
weights[18] +
" a19 *" +
weights[19] +
" a20 *" +
weights[20] +
" a21 *" +
weights[21] +
" a22 *" +
weights[22] +
" a23 *" +
weights[23] +
" a24 *" +
weights[24] +
" a25 *" +
weights[25] +
" a26 *" +
weights[26] +
" a27 *" +
weights[27] +
" a28 *" +
weights[28] +
" a29 *" +
weights[29] +
" a30 *" +
weights[30] +
" bias: " +
weights[31]);
//2d array for features
int x_TEST = 0;
int[][] feature_matrix_TEST = new int[ test_perceptron_input.size() ][ GLOBO_DICT.size() ];
for(Entry<File, int[]> entry : test_perceptron_input.entrySet())
{
int[] container =entry.getValue();
for(int jj = 0; jj < 4; jj++)
{
feature_matrix_TEST[x_TEST][jj] = container[jj];
}
x_TEST++;
}
//three variables (features) they enumerate by
//features, xyz, i also do that
double[] z00 = new double [NUM_INSTANCES];
double[] z01 = new double [NUM_INSTANCES];
double[] z02 = new double [NUM_INSTANCES];
double[] z03 = new double [NUM_INSTANCES];
double[] z04 = new double [NUM_INSTANCES];
double[] z05 = new double [NUM_INSTANCES];
double[] z06 = new double [NUM_INSTANCES];
double[] z07 = new double [NUM_INSTANCES];
double[] z08 = new double [NUM_INSTANCES];
double[] z09 = new double [NUM_INSTANCES];
double[] z10 = new double [NUM_INSTANCES];
double[] z11 = new double [NUM_INSTANCES];
double[] z12 = new double [NUM_INSTANCES];
double[] z13 = new double [NUM_INSTANCES];
double[] z14 = new double [NUM_INSTANCES];
double[] z15 = new double [NUM_INSTANCES];
double[] z16 = new double [NUM_INSTANCES];
double[] z17 = new double [NUM_INSTANCES];
double[] z18 = new double [NUM_INSTANCES];
double[] z19 = new double [NUM_INSTANCES];
double[] z20 = new double [NUM_INSTANCES];
double[] z21 = new double [NUM_INSTANCES];
double[] z22 = new double [NUM_INSTANCES];
double[] z23 = new double [NUM_INSTANCES];
double[] z24 = new double [NUM_INSTANCES];
double[] z25 = new double [NUM_INSTANCES];
double[] z26 = new double [NUM_INSTANCES];
double[] z27 = new double [NUM_INSTANCES];
double[] z28 = new double [NUM_INSTANCES];
double[] z29 = new double [NUM_INSTANCES];
double[] z30 = new double [NUM_INSTANCES];
for(int g = 0; g < NUM_INSTANCES; g++)
{
z00[g] = feature_matrix_TEST[g][ 0];
z01[g] = feature_matrix_TEST[g][ 1];
z02[g] = feature_matrix_TEST[g][ 2];
z03[g] = feature_matrix_TEST[g][ 3];
z04[g] = feature_matrix_TEST[g][ 4];
z05[g] = feature_matrix_TEST[g][ 5];
z06[g] = feature_matrix_TEST[g][ 6];
z07[g] = feature_matrix_TEST[g][ 7];
z08[g] = feature_matrix_TEST[g][ 8];
z09[g] = feature_matrix_TEST[g][ 9];
z10[g] = feature_matrix_TEST[g][10];
z11[g] = feature_matrix_TEST[g][11];
z12[g] = feature_matrix_TEST[g][12];
z13[g] = feature_matrix_TEST[g][13];
z14[g] = feature_matrix_TEST[g][14];
z15[g] = feature_matrix_TEST[g][15];
z16[g] = feature_matrix_TEST[g][16];
z17[g] = feature_matrix_TEST[g][17];
z18[g] = feature_matrix_TEST[g][18];
z19[g] = feature_matrix_TEST[g][19];
z20[g] = feature_matrix_TEST[g][20];
z21[g] = feature_matrix_TEST[g][21];
z22[g] = feature_matrix_TEST[g][22];
z23[g] = feature_matrix_TEST[g][23];
z24[g] = feature_matrix_TEST[g][24];
z25[g] = feature_matrix_TEST[g][25];
z26[g] = feature_matrix_TEST[g][26];
z27[g] = feature_matrix_TEST[g][27];
z28[g] = feature_matrix_TEST[g][28];
z29[g] = feature_matrix_TEST[g][29];
z30[g] = feature_matrix_TEST[g][30];
}
int output_TEST;
// calculate predicted class TEST
output_TEST = calculateOutput(theta,
weights,
z00[2],
z01[2],
z02[2],
z03[2],
z04[2],
z05[2],
z06[2],
z07[2],
z08[2],
z09[2],
z10[2],
z11[2],
z12[2],
z13[2],
z14[2],
z15[2],
z16[2],
z17[2],
z18[2],
z19[2],
z20[2],
z21[2],
z22[2],
z23[2],
z24[2],
z25[2],
z26[2],
z27[2],
z28[2],
z29[2],
z30[2]);
System.out.println("\n=======\nTEST Point:");
System.out.println(
"z00[0]:" + z00[0] +
"z01[0]:" + z01[0] +
"z02[0]:" + z02[0] +
"z03[0]:" + z03[0] +
"z04[0]:" + z04[0] +
"z05[0]:" + z05[0] +
"z06[0]:" + z06[0] +
"z07[0]:" + z07[0] +
"z08[0]:" + z08[0] +
"z09[0]:" + z09[0] +
"z10[0]:" + z10[0] +
"z11[0]:" + z11[0] +
"z12[0]:" + z12[0] +
"z13[0]:" + z13[0] +
"z14[0]:" + z14[0] +
"z15[0]:" + z15[0] +
"z16[0]:" + z16[0] +
"z17[0]:" + z17[0] +
"z18[0]:" + z18[0] +
"z19[0]:" + z19[0] +
"z20[0]:" + z20[0] +
"z21[0]:" + z21[0] +
"z22[0]:" + z22[0] +
"z23[0]:" + z23[0] +
"z24[0]:" + z24[0] +
"z25[0]:" + z25[0] +
"z26[0]:" + z26[0] +
"z27[0]:" + z27[0] +
"z28[0]:" + z28[0] +
"z29[0]:" + z29[0] +
"z30[0]:" + z30[0]
);
System.out.println("class = "+output_TEST);
}
//end main
/**
* returns a random double value within a given range
* @param min the minimum value of the required range (int)
* @param max the maximum value of the required range (int)
* @return a random double value between min and max
*/
public static double randomNumber(int min , int max) {
DecimalFormat df = new DecimalFormat("#.####");
double d = min + Math.random() * (max - min);
String s = df.format(d);
double x = Double.parseDouble(s);
return x;
}
/**
* returns either 1 or 0 using a threshold function
* theta is 0range
* @param theta an integer value for the threshold
* @param weights[] the array of weights
* @param x the x input value
* @param y the y input value
* @param z the z input value
* @return 1 or 0
*/
static int calculateOutput(int theta,
double weights[],
double a00,
double a01,
double a02,
double a03,
double a04,
double a05,
double a06,
double a07,
double a08,
double a09,
double a10,
double a11,
double a12,
double a13,
double a14,
double a15,
double a16,
double a17,
double a18,
double a19,
double a20,
double a21,
double a22,
double a23,
double a24,
double a25,
double a26,
double a27,
double a28,
double a29,
double a30)
{
double sum = a00 *
weights[ 0] +
a01 *
weights[ 1] +
a02 *
weights[ 2] +
a03 *
weights[ 3] +
a04 *
weights[ 4] +
a05 *
weights[ 5] +
a06 *
weights[ 6] +
a07 *
weights[ 7] +
a08 *
weights[ 8] +
a09 *
weights[ 9] +
a10 *
weights[10] +
a11 *
weights[11] +
a12 *
weights[12] +
a13 *
weights[13] +
a14 *
weights[14] +
a15 *
weights[15] +
a16 *
weights[16] +
a17 *
weights[17] +
a18 *
weights[18] +
a19 *
weights[19] +
a20 *
weights[20] +
a21 *
weights[21] +
a22 *
weights[22] +
a23 *
weights[23] +
a24 *
weights[24] +
a25 *
weights[25] +
a26 *
weights[26] +
a27 *
weights[27] +
a28 *
weights[28] +
a29 *
weights[29] +
a30 *
weights[30] +
weights[31];
return (sum >= theta) ? 1 : 0;
}
}
