I want to translate a merge function from the following JS code to us in RQL:
var d1 = {
del: {
1: {n: 1, v: 100, vFx:[100, 110]},
2: {n: 1, v: 100, vFx:[100, 110]}
}};
var d2 = {
del: {
2: {n: 1, v: 100, vFx:[100, 110]},
3: {n: 1, v: 100, vFx:[100, 110]}
}};
function merge(d1, d2) {
for(k in d2.del){
v = d2.del[k];
d1v = d1.del[k];
if(!d1v){
d1.del[k] = v;
} else{
d1v.n += v.n;
d1v.v += v.v;
for(var i = 0, _len = v.vFx.length; i < _len; i++)
d1v.vFx[i] += v.vFx[i];
}
}
};
// test
merge(d1, d2);
console.log(d1);
// GOAL
r.do(d1, d2, merge) // this of course doesn't work
My main problem is how to iterate through keys of a document? r.forEach requires me to make a save inside. My use case is that I want to use this merge function in update:
rqlexpr.update(merge(d1, r.row))
You can map over the keys of a document with keys, and construct an object with object. For example:
d1.merge(
r.object(
d2('del').keys().concatMap(function(key) {
return r.branch(
d1('del').hasField(key).not(),
[key, d2('del').getField(key)],
[key, d1('del').getField(key) + d2('del').getField(key)]
)
})
)
)
Related
I have a script that works mostly the way want. It looks at a cell then compares that to a column in another tab, finds the like items and returns that, and creates a dropdown on the cell. This moves down the column until it reaches the end. The problem is that it continues past the last row for about 20 rows. The starting row is row24.
function getInventoryItems() {
var jobSummaryInventoryItems = jobSummary.getRange(24, 8, jobSummary.getLastRow(), 1);
var jobSummaryInventoryItemsValues = jobSummaryInventoryItems.getValues();
var inventoryItems = inventory.getRange(4, 3, inventory.getLastRow(), 1);
var inventoryItemsValues = inventoryItems.getValues();
jobSummary.getRange(24, 8, jobSummary.getLastRow(), 1).setDataValidation(null);
for (z = 0; z < jobSummaryInventoryItemsValues.length; z++) {
if (jobSummaryInventoryItemsValues[z].toString().length > 1) {
var listOfInventory = [];
for (i = 0; i < inventoryItems.getLastRow() - 4; i++) {
if (inventoryItemsValues[i].toString() == jobSummaryInventoryItemsValues[z]) {
break;
}
var w = jobSummaryInventoryItemsValues[z];
if (inventoryItemsValues[i].toString().includes(jobSummaryInventoryItemsValues[z])) {
listOfInventory.push(inventoryItemsValues[i].toString());
}
}
}
if (listOfInventory.length > 0) {
var rangeRule = SpreadsheetApp.newDataValidation().requireValueInList(listOfInventory).build();
jobSummary.getRange(z + 24, 8).setDataValidation(rangeRule);
}
}
Get Inventory Items
function getInventoryItems() {
const ss = SpreadsheetApp.getActive();
const jobSummary = ss.getSheetByName('Job Summary');
const inventory = ss.getSheetByName('Inventory');
const jobSummaryInventoryItems = jobSummary.getRange(24, 8, jobSummary.getLastRow() - 23, 1);
const jobSummaryInventoryItemsValues = jobSummaryInventoryItems.getValues();
const inventoryItems = inventory.getRange(4, 3, inventory.getLastRow() - 3, 1);
const inventoryItemsValues = inventoryItems.getValues();
jobSummary.getRange(24, 8, jobSummary.getLastRow() -23, 1).setDataValidation(null);
for (z = 0; z < jobSummaryInventoryItemsValues.length; z++) {
if (jobSummaryInventoryItemsValues[z].toString().length > 1) {
let listOfInventory = [];
for (i = 0; i < inventoryItems.length; i++) {
if (inventoryItemsValues[i].toString() == jobSummaryInventoryItemsValues[z]) {
break;
}
let w = jobSummaryInventoryItemsValues[z];
if (inventoryItemsValues[i].toString().includes(jobSummaryInventoryItemsValues[z])) {
listOfInventory.push(inventoryItemsValues[i].toString());
}
}
}
if (listOfInventory.length > 0) {
let rangeRule = SpreadsheetApp.newDataValidation().requireValueInList(listOfInventory).build();
jobSummary.getRange(z + 24, 8).setDataValidation(rangeRule);
}
}
}
Sheet.getRange(row,column,number of rows, number of columns)
I'm trying to train a model and never pass the fit().
In the console doesn't show the loss result, it gets stuck there.
Already changed the async to a promise, but it's the same.
To see the entire code, click here!
function train() {
trainModel().then(result => {
console.log(result.history.loss[0]);
setTimeout(train, 100);
});
}
// entrena modelo~ params = train_xs(input) y train_ys(output)
async function trainModel() {
//Create the input data
for (let i = 0; i < 5; i++) {
train_xs = tf.tensor2d(ins.pixels[i], [28, 28], 'int32');
train_ys = tf.tensor2d(outs.coords[i], [3, 2], 'int32');
const h = await model.fit(train_xs, train_ys, {
epochs: 1
});
console.log("Loss after Epoch " + i + " : " + h.history.loss[0]);
}
console.log('end fitness model');
}
//never shows end fitness model
no error messages, the console keeps just clean
There are a couple of issues (the console is clean because it was not logging out the errors):
the shape of xs and ys does not match the input and output of the model.ins.pixels[i]
xs and ys should have the same batch size. Since in all iteration of the for loop, only one feature and one label is used, therefore the batchsize is 1.
Here is a fix of the model
let model;
let xs;
let train_xs;
let train_ys;
let inAndOut;
let resolution = 20;
let cols;
let rows;
var ins;
var outs;
function setup() {
createCanvas(400, 400);
/// visualization
ins = new Inputs13(); // ins.pixels;
outs = new Outputs13(); // outs.coords;
inAndOut = new InputsAndOutputsToTest();
///crear modelo
model = tf.sequential();
let hidden = tf.layers.dense({
inputShape: [784],
units: 28,
activation: 'sigmoid'
});
let output = tf.layers.dense({
units: 6,
activation: 'sigmoid'
});
model.add(hidden);
model.add(output);
const optimizer = tf.train.adam(0.1);
model.compile({
optimizer: optimizer,
loss: 'meanSquaredError'
})
xs = tf.tensor2d(inAndOut.pixelsToTest[0],[28,28]);
//console.log('xs');
//console.log(xs);
//xs.print();
//entrena modelo
setTimeout(train, 10);
}
//promesa, llama a entrenar modelo y muestra de losss
function train() {
console.log("im in train!");
trainModel().then(result => {
console.log(result.history.loss[0]);
setTimeout(train, 100);
});
}
// entrena modelo~ params = train_xs(input) y train_ys(output)
async function trainModel() {
let h;
//Create the input data
for (let i = 0; i < 5; i++) {
train_xs = tf.tensor(ins.pixels[i], [1, 784]); //[tens], [shape]
console.log('xs.shape', train_xs.shape)
train_ys = tf.tensor(outs.coords[i]).reshape([1, 6]);
console.log('ys.shape', train_ys.shape)
/* console.log('train_xs');
train_xs.print();
console.log("train_ys");
train_ys.print();*/
h = await model.fit(train_xs, train_ys, {
// shuffle: true,
epochs: 1
});
console.log("Loss after Epoch " + i + " : " + h.history.loss[0]);
}
console.log('end fitness model');
return h;
}
//muestra visual!
function draw() {
background(220);
//Get the predictions params xs = inputs para pruebas
tf.tidy(() => {
let ys = model.predict(xs);
//console.log("ys");
//console.log(ys);
let y_values = ys.dataSync();
// console.log("y_values");
// console.log(y_values);
});
}
However, it is possible to use all the 13 features and 13 labels all at once. The for loop will no longer be useful.
train_xs = tf.tensor(ins.pixels, [13, 784]);
console.log('xs.shape', train_xs.shape)
train_ys = tf.tensor(outs.coords).reshape([13, 6]);
My web app is generating an "Invalid Array Width" error at line 462 of Crossfilter.js v1.3.12. This error seems to tell me I have >32 dimensions. The puzzle is that I am not knowingly declaring a new dimension when the error occurs.
I have 10 slider bars, which act as numeric filters on my dataset. At the end of a drag event on the second slider bar, a dimension is declared if none already exists at the second location within the numericDims array. (Edit: even when I declare all the 10 dimensions in advance, and remove the dynamic declaration, the problem still occurs.) About 10 dimensions already exist in the app for other graphics & filters.
The first time I move a slider handle, "new dimension" is logged. After that, every time I move a handle on the same slider, "new dimension" is not logged. This is expected behaviour. But if I move the handles enough times, I get the "Invalid Array Width" error. So, I think I must be accidentally declaring a new dimension every time I move a handle. Can anyone see how I am unwittingly declaring a new dimension? The most relevant code:
if (!numericDims[tempIndex]) {
console.log('new dimension');
numericDims[tempIndex] = facts.dimension(function(p){ return p[d]; });
}
if (flag==0) {
prt.classed("activeFilter",true);
numericDims[tempIndex].filterFunction(function(p){ return p>=min && p<=max; });
} else {
prt.classed("activeFilter",false);
numericDims[tempIndex].filterAll();
// numericDims[tempIndex].dispose(); ***I figure it's quicker to store them instead of disposing/deleting. Even when I dispose/delete, the problem still happens.
// delete numericDims[tempIndex];
// numericDims.splice(tempIndex,1);
prt.selectAll("g.handle.left").attr("title",null);
prt.selectAll("g.handle.right").attr("title",null);
}
console.log(numericDims);
Full function:
function dragended(d) {
let transformation = {
Y: Math.pow(10, 24),
Z: Math.pow(10, 21),
E: Math.pow(10, 18),
P: Math.pow(10, 15),
T: Math.pow(10, 12),
G: Math.pow(10, 9),
M: Math.pow(10, 6),
k: Math.pow(10, 3),
h: Math.pow(10, 2),
da: Math.pow(10, 1),
d: Math.pow(10, -1),
c: Math.pow(10, -2),
m: Math.pow(10, -3),
μ: Math.pow(10, -6),
n: Math.pow(10, -9),
p: Math.pow(10, -12),
f: Math.pow(10, -15),
a: Math.pow(10, -18),
z: Math.pow(10, -21),
y: Math.pow(10, -24)
}
let reverse = s => {
let returnValue;
Object.keys(transformation).some(k => {
if (s.indexOf(k) > 0) {
returnValue = parseFloat(s.split(k)[0]) * transformation[k];
return true;
}
})
return returnValue;
}
var facts = window.facts;
if (d3.select(this).attr("class").indexOf("left")==-1) { var otherHandle = 'left'; } else { var otherHandle = 'right'; }
d3.select(this).classed("dragging",false);
var filterFields = window.filterFields;
var tempIndex = filterFields[0].indexOf(d);
var min = filterFields[2][tempIndex];
var max = filterFields[3][tempIndex];
//console.log(min+', '+max);
var scale = filterFields[4][tempIndex];
var t = d3.transform(d3.select(this).attr("transform"));
var thisX = t.translate[0];
var flag=0;
var prt = d3.select("g#f_"+tempIndex);
var leftHandleX = d3.transform(prt.selectAll("g.handle.left").attr("transform")).translate[0];
var rightHandleX = d3.transform(prt.selectAll("g.handle.right").attr("transform")).translate[0];
var wid = prt.selectAll("g.axis").select("rect.numFilterBox").attr("width");
prt.selectAll("g.axis").select("rect.numFilterBox").attr("x",leftHandleX).attr("width",rightHandleX - leftHandleX);
var num = -1;
var pFlag = 0;
if (filterFields[3][tempIndex]<=1) { var fmt = d3.format('%'); pFlag=1; } else { var fmt = d3.format('4.3s'); }
if (otherHandle=='left') {
if (thisX>=300 && scale(min)==0) { flag=1; }
max = scale.invert(thisX);
if (isNaN(+fmt(max).trim())) {
if (pFlag==1) {
max = +fmt(max).substr(0,fmt(max).length-1)/100
} else {
max = reverse(fmt(max));
}
} else {
max = +fmt(max).trim();
}
prt.selectAll("g.handle.right").attr("title",function(d){ return 'The filtered maximum for '+filterFields[1][tempIndex]+' is '+max; });
} else {
if (thisX<=0 && scale(max)==300) { flag=1; }
min = scale.invert(thisX);
if (isNaN(+fmt(min).trim())) {
if (pFlag==1) {
min = +fmt(min).substr(0,fmt(min).length-1)/100
} else {
min = reverse(fmt(min));
}
} else {
min = +fmt(min).trim();
}
prt.selectAll("g.handle.left").attr("title",function(d){ return 'The filtered minimum for '+filterFields[1][tempIndex]+' is '+min; });
}
filterFields[2][tempIndex] = min;
filterFields[3][tempIndex] = max;
window.filterFields = filterFields;
if (!numericDims[tempIndex]) {
console.log('new dimension');
numericDims[tempIndex] = facts.dimension(function(p){ return p[d]; });
}
if (flag==0) {
prt.classed("activeFilter",true);
numericDims[tempIndex].filterFunction(function(p){ return p>=min && p<=max; });
} else {
prt.classed("activeFilter",false);
numericDims[tempIndex].filterAll();
// numericDims[tempIndex].dispose();
// delete numericDims[tempIndex];
// numericDims.splice(tempIndex,1);
prt.selectAll("g.handle.left").attr("title",null);
prt.selectAll("g.handle.right").attr("title",null);
}
console.log(numericDims);
update();
doHighlight();
window.dragFlag=1;
}
Following the example here:
http://learningthreejs.com/blog/2011/12/10/constructive-solid-geometry-with-csg-js/
And using Three.js with https://github.com/chandlerprall/ThreeCSG, I'm trying to do 3D boolean operations on nodes from the model. Like for example if I have a wall with a window, I want to do invert() on that to get just the window.
I have a function that returns all the vertices of the polygons of a node, here's an example of vertices of an object without holes https://pastebin.com/8dhYzPwE.
I'm using ThreeCSG like this:
const geometryThree = new THREE.Geometry();
geometryThree.vertices.push(
...vertices
);
const geometryCsg = new ThreeBSP(geometryThree);
But that's what I'm getting in geometryCsg:
"{
"matrix": {
"elements": {
"0": 1,
"1": 0,
"2": 0,
"3": 0,
"4": 0,
"5": 1,
"6": 0,
"7": 0,
"8": 0,
"9": 0,
"10": 1,
"11": 0,
"12": 0,
"13": 0,
"14": 0,
"15": 1
}
},
"tree": {
"polygons": []
}
}"
I think it's because the geometry.faces.length is 0.
How can I make the vertices array to be a proper Three.Geometry such that the faces won't be empty? Geometry.elementsNeedsUpdate doesn't work...
Is there an example that uses polygons of a shape as an array of Vector3s and transforms that to csg?
I just worked on a demo using THREE csg: the Viewer meshes have an indexed array of vertices so you cannot create a BSP directly out of it. Also my code is using a web worker to process the meshes in order to keep the UI responsive with large models, so I need first to send the mesh data to the worker and reconstruct a simple THREE.Mesh on the worker side, the code looks like below:
// Sends component geometry to the web worker
postComponent (dbId) {
const geometry = this.getComponentGeometry(dbId)
const msg = {
boundingBox: this.getComponentBoundingBox(dbId),
matrixWorld: geometry.matrixWorld,
nbMeshes: geometry.meshes.length,
msgId: 'MSG_ID_COMPONENT',
dbId
}
geometry.meshes.forEach((mesh, idx) => {
msg['positions' + idx] = mesh.positions
msg['indices' + idx] = mesh.indices
msg['stride' + idx] = mesh.stride
})
this.worker.postMessage(msg)
}
// get geometry for all fragments in a component
getComponentGeometry (dbId) {
const fragIds = Toolkit.getLeafFragIds(
this.viewer.model, dbId)
let matrixWorld = null
const meshes = fragIds.map((fragId) => {
const renderProxy = this.viewer.impl.getRenderProxy(
this.viewer.model,
fragId)
const geometry = renderProxy.geometry
const attributes = geometry.attributes
const positions = geometry.vb
? geometry.vb
: attributes.position.array
const indices = attributes.index.array || geometry.ib
const stride = geometry.vb ? geometry.vbstride : 3
const offsets = geometry.offsets
matrixWorld = matrixWorld ||
renderProxy.matrixWorld.elements
return {
positions,
indices,
offsets,
stride
}
})
return {
matrixWorld,
meshes
}
}
// On the worker side reconstruct THREE.Mesh
// from received data and create ThreeBSP
function buildComponentMesh (data) {
const vertexArray = []
for (let idx=0; idx < data.nbMeshes; ++idx) {
const meshData = {
positions: data['positions' + idx],
indices: data['indices' + idx],
stride: data['stride' + idx]
}
getMeshGeometry (meshData, vertexArray)
}
const geometry = new THREE.Geometry()
for (var i = 0; i < vertexArray.length; i += 3) {
geometry.vertices.push(vertexArray[i])
geometry.vertices.push(vertexArray[i + 1])
geometry.vertices.push(vertexArray[i + 2])
const face = new THREE.Face3(i, i + 1, i + 2)
geometry.faces.push(face)
}
const matrixWorld = new THREE.Matrix4()
matrixWorld.fromArray(data.matrixWorld)
const mesh = new THREE.Mesh(geometry)
mesh.applyMatrix(matrixWorld)
mesh.boundingBox = data.boundingBox
mesh.bsp = new ThreeBSP(mesh)
mesh.dbId = data.dbId
return mesh
}
function getMeshGeometry (data, vertexArray) {
const offsets = [{
count: data.indices.length,
index: 0,
start: 0}
]
for (var oi = 0, ol = offsets.length; oi < ol; ++oi) {
var start = offsets[oi].start
var count = offsets[oi].count
var index = offsets[oi].index
for (var i = start, il = start + count; i < il; i += 3) {
const a = index + data.indices[i]
const b = index + data.indices[i + 1]
const c = index + data.indices[i + 2]
const vA = new THREE.Vector3()
const vB = new THREE.Vector3()
const vC = new THREE.Vector3()
vA.fromArray(data.positions, a * data.stride)
vB.fromArray(data.positions, b * data.stride)
vC.fromArray(data.positions, c * data.stride)
vertexArray.push(vA)
vertexArray.push(vB)
vertexArray.push(vC)
}
}
}
The complete code of my sample is there: Wall Analyzer and the live demo there.
I'd like to archieve behaviour similar to Pythons' groupby.
[1, 1, 2, 1].GroupBy() => [[1, 1], [2], [1]]
I think this is what you're looking for:
var data = new int[] { 1, 1, 2, 1 };
var results = Enumerable.Range(0, data.Count ())
.Where (i => i == 0 || data.ElementAt(i - 1) != data.ElementAt(i))
.Select (i => new
{
//Key = data.ElementAt(i),
Group = Enumerable.Repeat(
data.ElementAt(i),
data.Skip(i).TakeWhile (d => d == data.ElementAt(i)).Count ())
}
);
Here's an example of it running and the results: http://ideone.com/NJGQB
Here's a lazy, generic extension method that does what you want.
Code:
public static IEnumerable<IEnumerable<T>> MyGroupBy<T>(this IEnumerable<T> source)
{
using(var enumerator = source.GetEnumerator())
{
var currentgroup = new List<T>();
while (enumerator.MoveNext())
{
if (!currentgroup.Any() || currentgroup[0].Equals(enumerator.Current))
currentgroup.Add(enumerator.Current);
else
{
yield return currentgroup.AsReadOnly();
currentgroup = new List<T>() { enumerator.Current };
}
}
yield return currentgroup.AsReadOnly();
}
}
Test:
void Main()
{
var data = new int[] { 1, 1, 2, 1 };
foreach(var g in data.MyGroupBy())
Console.WriteLine(String.Join(", ", g));
}
Output:
1, 1
2
1