I'm new at this, I´m creating a field that solves N x N Matrixes, and I want the spinner to add more TextFields every time I change its value, so the user can select the size of the matrix and then solve it...However I don't know how to do this.
I've tried to attach a listener JavaFX spinner but it didn't work, and it has created some errors in the code.
The code in question...
public static void main(String[] args) {
// TODO Auto-generated method stub
launch(args);
}
public void start(Stage primaryStage) throws Exception{
Double[][] A = new Double[6][6];
Label tamanoML = new Label("Tamaño Matriz:");
Spinner tamanoMS =new Spinner(1,5,0,1);
int tamano=(int) tamanoMS.getValue();
int r;
Label[] campoL = new Label[6];
TextField campo [][] = new TextField[6][6];
Button resolverB = new Button("Resolver matrix");
resolverB.setOnAction((ActionEvent t) -> {
int n=(int) tamanoMS.getValue();
int cont=0;
for(int i=0; i<n; i++) {
for(int j=0; j<n+1; j++) {
A[i][j]=Double.parseDouble(campo[i][j].getText());
}
}
for (int a = 0; a < n; a++) {
Double temp = 0.0;
temp = A[cont][cont];
for (int y = 0; y < (n + 1); y++) {
A[cont][y] = A[cont][y] / temp;
}
for (int x = 0; x < n; x++) {
if (x!=cont) {
Double c = A[x][cont];
for (int z = 0; z < (n + 1); z++) {
A[x][z] = ((-1 * c) * A[cont][z]) + A[x][z];
}
}
}
cont++;
}
for (int i = 0; i < n; i++) {
for (int j = 0; j < n+1; j++) {
campo[i][j].setText(""+A[i][j]);
}
}
});
Button limpiarb = new Button("Limpiar");
limpiarb.setOnAction((ActionEvent t) -> {
for(int i=0; i<tamano+1; i++) {
for(int j=0; j<tamano+1; j++) {
campo[i][j].setText("0");
}
}
});
GridPane mainPane = new GridPane();
mainPane.setMinSize(650,350);
mainPane.setPadding(new Insets(10,10,10,10));
mainPane.setVgap(5);
mainPane.setHgap(5);
mainPane.setAlignment(Pos.CENTER);
mainPane.add(tamanoML, 0, 0);
mainPane.add(tamanoMS, 1, 0);
mainPane.add(resolverB,0,1);
mainPane.add(limpiarb,1,1);
for(int i=0; i<tamano+1; i++) {
r=i+1;
if(r<tamano+1) {
r=i+1;
campoL[i]=new Label("X"+r);
mainPane.add(campoL[i],i,2);
}else {
campoL[i]=new Label("R");
mainPane.add(campoL[i],i,2);
}
}
for(int i=0; i<tamano; i++) {
for(int j=0; j<tamano+1; j++) {
campo[i][j] = new TextField("0");
mainPane.add(campo[i][j],j,i+3);
}
}
tamanoMS.valueProperty().addListener((newValue) -> {
int s;
for(int i=0; i<tamano+1; i++) {
s=i+1;
if(s<tamano+1) {
s=i+1;
campoL[i]=new Label("X"+s);
mainPane.add(campoL[i],i,2);
}else {
campoL[i]=new Label("R");
mainPane.add(campoL[i],i,2);
}
}
for(int i=0; i<tamano; i++) {
for(int j=0; j<tamano+1; j++) {
campo[i][j] = new TextField("0");
mainPane.add(campo[i][j],j,i+3);
}
}
s=0;
});
Scene scene = new Scene(mainPane);
primaryStage.setScene(scene);
primaryStage.setTitle("Ventas del Dia");
primaryStage.show();
}
Ok so the first thing you want to do is rewrite your listener like this
tamanoMS.valueProperty().addListener((obs, oldValue, newValue) -> {
System.out.println("heard");
int s;
for(int i=0; i<newValue+1; i++) {
s=i+1;
if(s<newValue+1) {
s=i+1;
campoL[i]=new Label("X"+s);
mainPane.add(campoL[i],i,2);
}else {
campoL[i]=new Label("R");
mainPane.add(campoL[i],i,2);
}
}
for(int i=0; i<newValue; i++) {
for(int j=0; j<newValue+1; j++) {
campo[i][j] = new TextField("0");
mainPane.add(campo[i][j],j,i+3);
}
}
s=0;
});
I'm gonna be honest I don't quite understand why you need oldValue and obs but you do. However, the reason your matrix wasn't expanding was because you weren't updating tamano to the most recent number.
You also need to change your spinner to this so that is know the value that is coming out is an integer.
Spinner<Integer> tamanoMS =new Spinner<Integer>(1,5,0,1);
Currently I'm trying to replicate this wonderful project https://www.youtube.com/watch?v=u8pwmzUVx48, though with more minimum hardware, in which for the classification i'm using a neural network embbeded inside the arduino uno which had been trained offline in more stronger PC. The code of the classifier is here
#include <math.h>
#include <Wire.h>
double dataMagnitude[100];
double feature[7];
double feat_hid[10];
double output[4];
int classi;
const double w_hid[8][10] =
{
{18.9336670380822,11.9186055569093,-5.40114594311576,-21.1410711100689,-49.7798510124792,-14.8215222433047,-34.7308138535581,118.348601654702,-13.6275407707706,-11.6090487951753},
{-21.4994463865001,72.0672467700052,4.05328299052883,21.4875491464005,51.1604296852446,-15.8459578543758,6.30350750703905,-152.565733734759,12.8648040583067,13.8199895673734},
{8.66214515599865,-200.488705071393,-5.86973559011113,-23.4805286444925,-1.11016147795671,-3.04686605995311,-93.4143749063794,-73.8925691072615,-6.18427236042285,-10.9872535411407},
{-12.317892447947,-37.2154526807162,3.83978412266769,2.12866508710205,-11.9740446238331,10.2198116665101,41.9369083083022,63.2036147993661,1.40768362243561,15.4978881385563},
{7.58319670243546,161.895072542918,-2.3654701067923,1.91708846557259,-2.87224127392426,-16.5850302581614,45.2372430254377,34.255971511768,-2.30234070310014,-7.8952356779332},
{0.603971580989218,128.0244079393,0.628535912033608,-1.25788426737745,-0.204480047424961,-41.3372322514891,3.26860448943873,4.163893213914,0.443532051478394,-0.276136697942473},
{-3.63944154925129,18.3802540188974,0.975095285922393,-0.326187652485656,1.25521855841132,39.4877166809573,-15.3542772116519,-14.9546824078715,0.965532742621277,3.72386985716534},
{5.93321854314305,12.673690719929,-3.36580252980446,-21.2089241183081,-10.8980644878121,-7.29095431091201,-30.2240843969778,-2.48980394834853,-5.4167647620581,-5.68671825319015}
}, w_out[11][4] =
{
{1.07887052819228,-21.9615926704441,105.450147012522,-84.5674248703326},
{0.0344508533215184,0.551350792323934,-0.96380329372866,0.37800164808339},
{-99.251397621058,23.1671754381478,7.53147169676884,68.5527504861813},
{-5.0354869957171,4.36918523413481,0.408725687040089,0.257576074543764},
{-27.4478368365984,7.00659524306471,1.74370945871769,18.6975321348269},
{-0.213736004615516,-0.784795805217531,0.0732416484342244,0.925290161399087},
{8.62052547929066,-45.9408034639393,116.959261953552,-79.6389839689755},
{-8.5317661802465,45.4251911929737,-117.146523754862,80.2530987422069},
{127.053878005091,-29.4397580015468,-9.33919798608733,-88.2749220175082},
{1.11869995358251,-21.5111648695486,105.002356379838,-84.6098914639344},
{-5.81786935588552,3.78305066207264,0.11556429335063,-0.0807455995360207}
};
double S, Sig, Sigma, Sigma1, Sigma2, MAV, RMS, VAR, SDT, WL, ZC, SSC, Xn, accum, accumi;
char analogpin = 0, N=100;
void setup()
{
/* add setup code here */
Serial.begin(9600);
Wire.begin();
}
void loop()
{
do{
//data acqusition
for( int i = 0; i<100;i++)
{
Xn = (analogRead(analogpin))-510;
dataMagnitude[i]=Xn;
delayMicroseconds(830);
// Serial.println(dataMagnitude[i]);
}
S = 0;
for (size_t i = 0; i < N; i++)
{
S = S + dataMagnitude[i];
}
Sig = 0;
Sigma = 0;
Sigma1 = 0;
Sigma2 = 0;
for (size_t i = 0; i < N; i++)
{
Sig = Sig + abs(dataMagnitude[i]);
Sigma = Sigma + pow(dataMagnitude[i], 2);
Sigma1 = Sigma1 + pow((dataMagnitude[i] - S / N), 2);
}
for (size_t i = 0; i < N - 1; i++)
{
Sigma2 = Sigma2 + abs(dataMagnitude[i + 1] - dataMagnitude[i]);
}
//featureextract
MAV = (((1 / (double)N)*Sig-27.67)*2/(272.02-27.67))-1;
RMS = (sqrt((1 / (double)N)*Sigma)-34.91002721)*2/(284.1419012-34.91002721)-1;
VAR = (((1 / (double)(N))*Sigma1)-698.4139)*2/(52178.5691-698.4139)-1;
SDT = (sqrt((1 / (double)(N)) * Sigma1)-26.42752164)*2/(228.4262881-26.42752164)-1;
WL = (Sigma2-1621)*2/(11273-1621)-1;
//ZC = 0;
for (size_t i = 0; i < N - 1; i++)
{
if (dataMagnitude[i] == 0 && dataMagnitude[i] != dataMagnitude[i + 1])
{
ZC++;
}
}
ZC = (ZC-0)*2/(39-0)-1;
//SSC = 0;
for (size_t i = 0; i < N - 2; i++)
{
if (dataMagnitude[i]>dataMagnitude[i + 1] && dataMagnitude[i + 1]<dataMagnitude[i + 2])
{
SSC++;
}
else if (dataMagnitude[i]<dataMagnitude[i + 1] && dataMagnitude[i + 1]>dataMagnitude[i + 2])
{
SSC++;
}
}
SSC = (SSC-48)*2/(78-48)-1;
double feature[] = { MAV, RMS, VAR, SDT, WL, ZC, SSC };
//neural network construction
//first-hidden layer
for (int i = 0; i < 10; i++)
{
accum = w_hid[7][i];
for (int j = 0; j < 7; j++)
{
accum += (feature[j] * w_hid[j][i]);
}
feat_hid[i] = tanh(accum);
}
for (int i = 0; i < 4; i++)
{
accumi = w_out[10][i];
for (int j = 0; j < 10; j++)
{
accumi += (feat_hid[j] * w_out[j][i]);
}
output[i] = round(accumi);
}
//Classify the output
if (output[0] == 1 && output[1] < 1 && output[2] < 1 && output[3] < 1)
{
classi = 1;
}
else if (output[0] < 1 && output[1] == 1 && output[2] < 1 && output[3] < 1)
{
classi = 2;
}
else if (output[0] < 1 && output[1] < 1 && output[2] == 1 && output[3] < 1)
{
classi = 3;
}
else if (output[0] < 1 && output[1] < 1 && output[2] < 1 && output[3] == 1)
{
classi = 4;
}
else
{
classi = 0;
}
Wire.beginTransmission(5);
Wire.write(classi);
//Wire.write(int(output[2]));
Wire.endTransmission();
Serial.println("wew");
Serial.println(output[0]);
Serial.println(output[1]);
Serial.println(output[2]);
Serial.println(output[3]);
//Serial.println(classi);
//Serial.println(feature[1]);
//Serial.println(feature[2]);
//Serial.println(feature[3]);
//Serial.println(feature[4]);
//Serial.println(feature[5]);
//Serial.println(feature[6]);
for (size_t i = 0; i < 10; i++)
{
feat_hid[i] = 0;
}
for (size_t i = 0; i < 4; i++)
{
output[i] = 0;
}
}while(analogRead(analogpin)>0);
}
But the real time implementation is rather dissatisfying, in which the output always mismatched, though by offline implementation is quite better.
My assumption is that the data capturing when online is rather bad, or maybe because any other things?
Thanks for any feedback
How did you train the network offline?
If you have used Matlab to train the network (i.e. to identify the weight and bias values) and then implement the same network on Arduino.
In such case we have problems with number precision. Matlab by default works on 64 bit precision whereas Arduino works on 32 bit.
Difference in the way the numbers are represented creates the Error.
Hey I am trying to multiply two dimensional arrays and I am confused. I have this code, but It Doesn't seem to work..
class MatrixMultiply {
public static void main(String[] args) {
int array[][] = { {1,2,-2,0}, {-3,4,7,2}, {6,0,3,1} };
int array1[][] = { {-1,3}, {0,9}, {1,-11}, {4,-5} };
int array2[][] = new int[2][3];
int x= array.length;
int y= array1.length;
for(int i = 0; i < x; i++) {
for(int j = 0; j < y-1; j++) {
for(int k = 0; k < y; k++){
array2[i][j] += array[i][k]*array1[k][j];
}
}
}
System.out.println("After we multiply the two arrays: ");
for(int i = 0; i < x; i++) {
for(int j = 0; j < y-1; j++) {
System.out.print(" "+array2[i][j]);
}
System.out.println();
}
}
}
modify your loop that multiplies the array.
for (int i = 0; i < x-1; i++) {
for (int j = 0; j < array1[0].length; j++) {
for (int k = 0; k < array[0].length; k++) {
array2[i][j] += array[i][k] * array1[k][j];
}
}
}
A simple query , i want to populate the dropdownlist with number starting from 17 to 90 , and the last number should be a string like 90+ instead of 90. I guess the logic will be using a for loop something like:
for (int a = 17; a <= 90; a++)
{
ddlAge.Items.Add(a.ToString());
}
Also I want to populate the text and value of each list item with the same numbers.
Any ideas?
for (int i = 17; i < 90; i++)
{
ddlAge.Items.Add(new ListItem(i.ToString(), i.ToString()));
}
ddlAge.Items.Add(new ListItem("90+", "90"));
Try this:
for (int a = 17; a <= 90; a++)
{
var i = (a == 90 ? a.ToString() + '+': a.ToString());
ddlAge.Items.Add(new ListItem(i, i));
}
This is easy enough. You need to instantiate the ListItem class and populate its properties and then add it to your DropDownList.
private void GenerateNumbers()
{
// This would create 1 - 10
for (int i = 1; i < 11; i++)
{
ListItem li = new ListItem();
li.Text = i.ToString();
li.Value = i.ToString();
ddlAge.Items.Add(li);
}
}
for (int a = 17; a <= 90; a++)
{
ddlAge.Items.Add(new ListItem(a.ToString(), a.ToString()));
}
for (int i = 17; i <= 90; i++)
{
ddlAge.Items.Add(new ListItem(i.ToString(), i.ToString()));
}
ddlAge.Items.Insert(0, new ListItem("Select Age", "0")); //First Item
ddlAge.Items.Insert(ddlAge.Items.Count, new ListItem("90+", "90+")); //Last Item
for (int i = 0; i <=91; i++)
{
if (i == 0)
{
ddlAge.Items.Add("Select Age");
}
else if(i<=90)
{
ddlAge.Items.Add(i.ToString());
i++;
}
else
{
ddlAge.Items.Add("90+");
}
}