Algodoo

MS Edge Copilot AI prompt:
I'm building a neural network in Algodoo using Thyme scripting (see 284446_.html). My _forwardPass function is throwing List index out of bounds errors during weight access and array slicing. Please rewrite _forwardPass with full Thyme safety and structure, following Thyme rules (no // comments, use full ternary syntax, no +=, use _xFor, Thyme statements end in ";", and check all array bounds). Also include troubleshooting tips for common indexing mistakes in Thyme.

MS Edge Copilot AI partial response below:

_forwardPass := {
console.print("(DEBUG) forward pass started");

layerCount := string.length(_layerSizes);
layerCount > 1 ? {
_xFor(0, layerCount - 2, (i) => {
i >= 0 && i < layerCount - 1 ? {
newLayer := [];
j := i + 1;

targetSize := _layerSizes(j);
sourceSize := _layerSizes(i);

targetSize > 0 && sourceSize > 0 ? {
_xFor(0, targetSize - 1, (k) => {
neuron := 0;

_xFor(0, sourceSize - 1, (l) => {
(i >= 0 && i < string.length(_network)) &&
(l >= 0 && l < string.length(_network(i))) &&
(i >= 0 && i < string.length(_weights)) &&
(k * sourceSize + l >= 0 && k * sourceSize + l < string.length(_weights(i))) ? {
neuron = neuron + (_network(i)(l) * _weights(i)(k * sourceSize + l));
} : {
console.print("(WARN) Skipped invalid index at layer " + i + ", neuron " + l + ", weight index " + (k * sourceSize + l));
};
});

(i >= 0 && i < string.length(_biases)) &&
(k >= 0 && k < string.length(_biases(i))) ? {
neuron = neuron + _biases(i)(k);
} : {
console.print("(WARN) Skipped bias index " + k + " at layer " + i);
};

newLayer = newLayer ++ [neuron];
});

_network = _xSetArr(_network, j, newLayer);
} : {
console.print("(WARN) Invalid layer sizes at index " + i);
};
} : {
console.print("(WARN) Invalid layer index " + i);
};
});
} : {
console.print("(WARN) Not enough layers to perform forward pass");
};

_network;
};

If you want, I can also give you a **minimal debug mode toggle** so you can turn logging on/off without editing every `console.print` line. That way you can run it silently once it’s stable. Would you like me to add that?

Yes.

Got it — here’s the **no‑comment, Thyme‑safe `_forwardPass`** with:

- **Auto‑repair** for `_network`, `_weights`, `_biases`
- **Detailed debug logging**
- **Debug mode toggle** so you can silence or enable logs without editing every `console.print`

You just set `debugMode := 1;` to see logs, or `debugMode := 0;` to run quietly.

_forwardPass := {
debugMode := 1;

debugMode == 1 ? { console.print("(DEBUG) forward pass started") } : {};

layerCount := string.length(_layerSizes);

string.length(_network) != layerCount ? {
debugMode == 1 ? { console.print("(FIX) Adjusting _network to match _layerSizes length") } : {};
fixedNet := [];
_xFor(0, layerCount - 1, (li) => {
expectedSize := _layerSizes(li);
(li < string.length(_network)) ? {
layerArr := _network(li);
string.length(layerArr) != expectedSize ? {
debugMode == 1 ? { console.print("(FIX) Resizing _network(" + li + ") from " + string.length(layerArr) + " to " + expectedSize) } : {};
adj := [];
_xFor(0, expectedSize - 1, (ni) => {
adj = adj ++ [(ni < string.length(layerArr) ? layerArr(ni) : 0)];
});
fixedNet = fixedNet ++ [adj];
} : {
fixedNet = fixedNet ++ [layerArr];
};
} : {
debugMode == 1 ? { console.print("(FIX) Adding missing layer " + li + " size " + expectedSize) } : {};
newArr := [];
_xFor(0, expectedSize - 1, (ni) => { newArr = newArr ++ [0] });
fixedNet = fixedNet ++ [newArr];
};
});
_network = fixedNet;
} : {};

string.length(_weights) != layerCount - 1 ? {
debugMode == 1 ? { console.print("(FIX) Adjusting _weights count") } : {};
fixedW := [];
_xFor(0, layerCount - 2, (wi) => {
expectedLen := _layerSizes(wi) * _layerSizes(wi + 1);
(wi < string.length(_weights)) ? {
wArr := _weights(wi);
string.length(wArr) != expectedLen ? {
debugMode == 1 ? { console.print("(FIX) Resizing _weights(" + wi + ") from " + string.length(wArr) + " to " + expectedLen) } : {};
adj := [];
_xFor(0, expectedLen - 1, (wi2) => {
adj = adj ++ [(wi2 < string.length(wArr) ? wArr(wi2) : 0)];
});
fixedW = fixedW ++ [adj];
} : {
fixedW = fixedW ++ [wArr];
};
} : {
debugMode == 1 ? { console.print("(FIX) Adding missing weights for layer " + wi) } : {};
newW := [];
_xFor(0, expectedLen - 1, (wi2) => { newW = newW ++ [0] });
fixedW = fixedW ++ [newW];
};
});
_weights = fixedW;
} : {};

string.length(_biases) != layerCount - 1 ? {
debugMode == 1 ? { console.print("(FIX) Adjusting _biases count") } : {};
fixedB := [];
_xFor(0, layerCount - 2, (bi) => {
expectedLen := _layerSizes(bi + 1);
(bi < string.length(_biases)) ? {
bArr := _biases(bi);
string.length(bArr) != expectedLen ? {
debugMode == 1 ? { console.print("(FIX) Resizing _biases(" + bi + ") from " + string.length(bArr) + " to " + expectedLen) } : {};
adj := [];
_xFor(0, expectedLen - 1, (bi2) => {
adj = adj ++ [(bi2 < string.length(bArr) ? bArr(bi2) : 0)];
});
fixedB = fixedB ++ [adj];
} : {
fixedB = fixedB ++ [bArr];
};
} : {
debugMode == 1 ? { console.print("(FIX) Adding missing biases for layer " + bi) } : {};
newB := [];
_xFor(0, expectedLen - 1, (bi2) => { newB = newB ++ [0] });
fixedB = fixedB ++ [newB];
};
});
_biases = fixedB;
} : {};

layerCount > 1 ? {
_xFor(0, layerCount - 2, (i) => {
debugMode == 1 ? { console.print("(DEBUG) Processing layer " + i) } : {};
newLayer := [];
j := i + 1;
targetSize := _layerSizes(j);
sourceSize := _layerSizes(i);
debugMode == 1 ? { console.print("(DEBUG) sourceSize=" + sourceSize + ", targetSize=" + targetSize) } : {};
(targetSize > 0 && sourceSize > 0) ? {
_xFor(0, targetSize - 1, (k) => {
neuron := 0;
_xFor(0, sourceSize - 1, (l) => {
weightIndex := k * sourceSize + l;
debugMode == 1 ? { console.print("(DEBUG) Accessing: layer=" + i +
", neuronIndex=" + l +
", weightIndex=" + weightIndex +
", netLayerLen=" + string.length(_network(i)) +
", weightsLen=" + string.length(_weights(i))) } : {};
(i >= 0 && i < string.length(_network)) &&
(l >= 0 && l < string.length(_network(i))) &&
(i >= 0 && i < string.length(_weights)) &&
(weightIndex >= 0 && weightIndex < string.length(_weights(i))) ? {
neuron = neuron + (_network(i)(l) * _weights(i)(weightIndex));
} : {
debugMode == 1 ? { console.print("(WARN) Skipped invalid index at layer " + i +
", neuron " + l +
", weightIndex " + weightIndex) } : {};
};
});
debugMode == 1 ? { console.print("(DEBUG) Adding bias for neuron " + k) } : {};
(i >= 0 && i < string.length(_biases)) &&
(k >= 0 && k < string.length(_biases(i))) ? {
neuron = neuron + _biases(i)(k);
} : {
debugMode == 1 ? { console.print("(WARN) Skipped bias index " + k + " at layer " + i) } : {};
};
newLayer = newLayer ++ [neuron];
});
_network = _xSetArr(_network, j, newLayer);
} : {
debugMode == 1 ? { console.print("(WARN) Invalid layer sizes at index " + i) } : {};
};
});
} : {
debugMode == 1 ? { console.print("(WARN) Not enough layers to perform forward pass") } : {};
};

_network;
};

it seems the AI made things overcomplicated.i know this bcs when i made the forward pass work layer-by-layer, it worked.

AI is overcomplicated because it has extra code to show or handle the indexing error source. My recollection was that the AI code ran without error. Once you locate the source of the error, you can modify your original code. What happened when you ran the AI code?