I'm working with a Spreadsheet where I need to get the values of 2 ranges (in arrays) and return the sum of those.
I found code and tried to plug it in, but it seems to be just concatenating the 2 arrays instead of actually summing them up.
The data I'm pulling is from my spreadsheet. The arrays are 5 Columns and 23 rows. The arrays are of the same size.
Here is my function that grabs the values of each arrays. It will then run it through the Arrays_sum function I found and return and update the table with the new totals.
function updateBowl(){
var row = mainSheet.getActiveCell().getRow();
var tagCol = mainSheet.getRange("HP52");
var rowNum = mainSheet.getRange("HO52");
tagCol.setValue("N4:N" + row);
rowNum.setValue(row);
var humpedData = mainSheet.getRange("HL54:HP77").getValues();
var processedTable = mainSheet.getRange("ID54:IH77");
var currentData = processedTable.getValues();
var newTotals = Arrays_sum(humpedData,currentData);
var setNewTotals = processedTable.setValues(newTotals);
Logger.log("New Totals: " + newTotals);
}
This is a function I found that supposedly sums up each array that's plugged into it, but it is not working for me.
function Arrays_sum(array1, array2)
{
var result = [];
var ctr = 0;
var x=0;
if (array1.length === 0)
return "array1 is empty";
if (array2.length === 0)
return "array2 is empty";
while (ctr < array1.length && ctr < array2.length)
{
result.push(array1[ctr] + array2[ctr]);
ctr++;
}
if (ctr === array1.length)
{
for (x = ctr; x < array2.length; x++) {
result.push(array2[x]);
}
}
else
{
for (x = ctr; x < array1.length; x++)
{
result.push(array1[x]);
}
}
return result;
}
Any help would be appreciated!
Edit 1: Pasted picture of the log.
Edit 2: In my log picture the first 1386 value is from the first cell in the FIRST array.
The second 1386 is the first value in SECOND array.
So it seems to concatenating the first row array with the second row array.
For my testing purposes the values are the same (because of lazy) but when I can figure out the array sum, the current values and incoming values will be different.
SOLVED
Coopers answer worked. I'm not sure exactly what I tweaked to get it to work but this is the final working script.
It gets 2 different arrays (of the same size) and sums the values in each cell, then pastes those values into the second array (the current totals).
function updateBowl(){
var row = mainSheet.getActiveCell().getRow();
var tagCol = mainSheet.getRange("HP52");
var rowNum = mainSheet.getRange("HO52");
tagCol.setValue("N4:N" + row);
rowNum.setValue(row);
var humpedData = mainSheet.getRange("HL54:HP77").getValues();
var processedTable = mainSheet.getRange("ID54:IH77");
var currentData = processedTable.getValues();
var newTotals = sumarrays(humpedData,currentData);
var setNewTotals = processedTable.setValues(newTotals);
Logger.log("New Totals: " + newTotals);
}
function sumarrays(arr1,arr2) {
var o=[];
var html='[';
arr1.forEach(function(r,i) {
o[i]=[];
if(i>0){html+=','};
html+='[';
r.forEach(function(c,j){
if(j>0){html+=','};
o[i][j]=arr1[i][j]+arr2[i][j];
html+=o[i][j];
});
html+=']';
});
html+=']';
return o;
SpreadsheetApp.getUi().showModelessDialog(HtmlService.createHtmlOutput(html), 'Output');
}
Try something like this:
function arraytest() {
const ss=SpreadsheetApp.getActive();
const sh=ss.getSheetByName('Sheet1');
const a1=sh.getRange(1,1,9,9).getValues();
const a2=sh.getRange(1,10,9,9).getValues();
sumarrays(a1,a2);
}
function sumarrays(arr1,arr2) {
var o=[];
var html='[';
arr1.forEach(function(r,i) {
o[i]=[];
if(i>0){html+=','};
html+='[';
r.forEach(function(c,j){
if(j>0){html+=','};
o[i][j]=arr1[i][j]+arr2[i][j];
html+=o[i][j];
});
html+=']';
});
html+=']';
return o;
SpreadsheetApp.getUi().showModelessDialog(HtmlService.createHtmlOutput(html), 'Output');
}
Data:
1,10,19,28,37,46,55,64,73,82,91,100,109,118,127,136,145,154,163,172,181
2,11,20,29,38,47,56,65,74,83,92,101,110,119,128,137,146,155,164,173,182
3,12,21,30,39,48,57,66,75,84,93,102,111,120,129,138,147,156,165,174,183
4,13,22,31,40,49,58,67,76,85,94,103,112,121,130,139,148,157,166,175,184
5,14,23,32,41,50,59,68,77,86,95,104,113,122,131,140,149,158,167,176,185
6,15,24,33,42,51,60,69,78,87,96,105,114,123,132,141,150,159,168,177,186
7,16,25,34,43,52,61,70,79,88,97,106,115,124,133,142,151,160,169,178,187
8,17,26,35,44,53,62,71,80,89,98,107,116,125,134,143,152,161,170,179,188
9,18,27,36,45,54,63,72,81,90,99,108,117,126,135,144,153,162,171,180,189
Output:
[[83,101,119,137,155,173,191,209,227],[85,103,121,139,157,175,193,211,229],[87,105,123,141,159,177,195,213,231],[89,107,125,143,161,179,197,215,233],[91,109,127,145,163,181,199,217,235],[93,111,129,147,165,183,201,219,237],[95,113,131,149,167,185,203,221,239],[97,115,133,151,169,187,205,223,241],[99,117,135,153,171,189,207,225,243]]
You can put constraints on it depending upon how the data is collected.
I hope this script will be an answer and a guide.
You can use this inside your spreadsheet as a normal function. Like this:
=arr_arr(A1:D5,"+",F6:K9)
The code:
/**
* Return the sum of total array one + array two
*
* #param {A1:D10} range - First range to sum.
* #param {"+ - / *"} operator - Operator to use.
* #param {E1:F10} range - Second range to sum.
* #return the sum of all the values
* #customfunction
*/
function arr_arr(range1,op,range2) {
const one = [].concat(...range1);
const two = [].concat(...range2);
const sumOne = one.reduce((a, b) => a + b, 0);
const sumTwo = two.reduce((a, b) => a + b, 0);
let sum = 0;
switch (op) {
case "+":
sum = sumOne + sumTwo;
break;
case "-":
sum = sumOne - sumTwo;
break;
case "*":
sum = sumOne * sumTwo;
break;
case "/":
sum = sumOne / sumTwo;
break;
}
return sum;
}
When I have been attempting to implement this function tf.train.stg(learningRate).minimize(loss)into my code in order to conduct back-propagation. I have been getting multiple errors such The f passed in variableGrads(f) must be a function. How would I implement the function above into the code bellow successfully? and Why does this error even occur?
Neural Network:
var X = tf.tensor([[1,2,3], [4,5,6], [7,8,9], [10,11,12]])
var Y = tf.tensor([[0,0,0],[0,0,0], [1,1,1]])
var m = X.shape[0]
var a0 = tf.zeros([1,3])
var y_hat = tf.zeros([1,3])
var parameters = {
"Wax": tf.randomUniform([1,3]),
"Waa": tf.randomUniform([3,3]),
"ba": tf.zeros([1,3]),
"Wya": tf.randomUniform([3,3]),
"by": tf.zeros([1,3])
}
function RNN_cell_Foward(xt, a_prev, parameters){
var Wax = parameters["Wax"]
var Waa = parameters["Waa"]
var ba = parameters["ba"]
var a_next = tf.sigmoid(tf.add(tf.add(tf.matMul(xt, Wax), tf.matMul(a_prev , Waa)),ba))
return a_next
}
function RNN_FowardProp(X, a0, parameters){
var T_x = X.shape[0]
var a_next = a0
var i = 1
var Wya = parameters["Wya"]
var by = parameters["by"]
var l = 1
for(; i <= T_x; i++){
var X_i = X.slice([i-1,0],[1,-1])
for(; l <= X.shape[1]; l++){
var xt = X_i.slice([0,l-1],[1,1])
var a_next = RNN_cell_Foward(xt, a_next, parameters)
}
var y_pred = tf.sigmoid((tf.add(tf.matMul(a_next, Wya), by)))
l = 1
if (i == 1){
var y_pred1 = y_pred
} else if (i == 2) {
var y_pred2 = y_pred
} else if (i == 3) {
var y_pred3 = y_pred
}
}
var y_predx = tf.concat([y_pred1, y_pred2, y_pred3])
return y_predx
}
const learningRate = 0.01;
var optimizer = tf.train.sgd(learningRate);
var model = RNN_FowardProp(X, a0, parameters)
var loss = tf.losses.meanSquaredError(Y, model)
for (let f = 0; f < 10; f++) {
optimizer.minimize(loss)
}
This is a neural network for sentiment classification which has a many to one structure.
The error says it all:
The f passed in variableGrads(f) must be a function
optimizer.minimize is expecting a function as parameter and not a tensor. Since the code is trying to minimize the meanSquaredError, the argument of minimize can be a function that computes the meanSquaredError between the predicted value and the expected one.
const loss = (pred, label) => pred.sub(label).square().mean();
for (let f = 0; f < 10; f++) {
optimizer.minimize(() => tf.losses.meanSquaredError(Y, model))
}
Does it solve the issue, not completely yet ? The error will change for something like:
variableGrads() expects at least one of the input variables to be trainable
What does it mean ? When the optimizer is used, it expects the function passed as argument to contains variables whose values will be updated to minimize the function output.
Here is the changes to be made:
var Y = tf.tensor([[0,0,0],[0,0,0], [1,1,1]]).variable() // a variable instead
// var loss = tf.losses.meanSquaredError(Y, model)
// computed below in the minimize function
const learningRate = 0.01;
var optimizer = tf.train.sgd(learningRate);
var model = RNN_FowardProp(X, a0, parameters);
const loss = (pred, label) => pred.sub(label).square().mean();
for (let f = 0; f < 10; f++) {
optimizer.minimize(() => tf.losses.meanSquaredError(Y, model))
}
I am working with Col A, B & C. Col A contains A-E, Col B Contains 1, a, 3, b, 5 and Col C will be where I will store duplicated information (a and b would go into C1 & C2). Any help would be appreciated. In summary; compare A and B for similarity, output result into C
function appendString() {
var range = SpreadsheetApp.getActiveSheet().getRange("A1:A5");
var range2 = SpreadsheetApp.getActiveSheet().getRange("B1:B5");
var range3 = SpreadsheetApp.getActiveSheet().getRange("C1:C5")
var numRows = range.getNumRows();
var x = 0
// var numCols = range.getNumColumns();
j = 1 // row A
k = 2 // row B
m = 3 // row C
n = 1
// First loop though B
for (var i = 1; i <= numRows; i++) {
// set the current value...
var currentValue = range.getCell(i, j).getValue();
// In the log tell us the current value
Logger.log("Set A:" + currentValue);
// Loops though col B to compare to col A
for (var l = 1; l <= numRows; l++) {
// Sets the current value to compare value
var compareValue = range2.getCell(l, j).getValue();
Logger.log("Set B:" + compareValue)
// If the compareValue and the currentValue (match)
if (compareValue === currentValue) {
Logger.log("MATCHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH");
// We will write the result to col C down one row
for (n; n <= x; n++) {
// this makes it only run once'
range3.setValue(currentValue);
Logger.log("Appending.................");
x = n + 3
}
}
}
}
}
I think your problem statement boils down to this: Fill column C with a list of unique values that appear in both column A and B.
There is a built-in javascript Array method Array.indexOf() that makes it very easy to search for matching elements. As the problem is defined, we want to search in a column, so to use that method we need a column to be represented as an Array. The Range.getValues() method allows us to load a whole range of values at once, and delivers them as a two-dimensional array, with rows as the first dimension. We need columns there, and we can achieve that by a matrix transposition.
So here's what we end up with. There isn't a built-in transpose(), so I've included one. As we search for matches, results are stored in an Array C, using the built-in Array.push() method. Finally, array C is treated as a two-dimensional array, transposed, and written out to the sheet in column C.
function recordMatches() {
var range = SpreadsheetApp.getActiveSheet().getRange("A1:B5");
var data = range.getValues();
// For convenience, we'll transpose the data, so
// we can treat columns as javascript arrays.
var transposed = transpose(data);
var A = transposed[0],
B = transposed[1],
C = [];
// Go through A, looking for matches in B - if found, add match to C
for (var i=0; i < A.length; i++) {
if (B.indexOf(A[i]) !== -1) C.push(A[i]);
}
// If any matches were found, write the resulting array to column C
if (C.length > 0) {
var rangeC = SpreadsheetApp.getActiveSheet().getRange(1,3,C.length);
rangeC.setValues(transpose([C]));
}
}
function transpose(a) {
return Object.keys(a[0]).map(function (c) { return a.map(function (r) { return r[c]; }); });
}