10 PRINT "QUANTIZATION DEMO"
20 PRINT "----------------"
30 PRINT
40 PRINT "Training and Testing Model..."
50 PRINT

60 REM Training the Model
70 REM Here, you would typically train your machine learning model using a dataset, but for simplicity, we'll define a sample model weights.
80 REM Assume the model is a simple neural network with 3 layers and each layer has 10 weights.

90 DIM weightsLayer1(10), weightsLayer2(10), weightsLayer3(10)
100 REM Fill weights with random values for demonstration purposes
110 FOR i = 0 TO 10
120 weightsLayer1(i) = RND(1)
130 weightsLayer2(i) = RND(1)
140 weightsLayer3(i) = RND(1)
150 NEXT i

160 PRINT "Original Model Weights:"
170 PRINT
180 PRINT "Layer 1 Weights: ", weightsLayer1(0), weightsLayer1(1), weightsLayer1(2), weightsLayer1(3), weightsLayer1(4), weightsLayer1(5), weightsLayer1(6), weightsLayer1(7), weightsLayer1(8), weightsLayer1(9), weightsLayer1(10)
190 PRINT "Layer 2 Weights: ", weightsLayer2(0), weightsLayer2(1), weightsLayer2(2), weightsLayer2(3), weightsLayer2(4), weightsLayer2(5), weightsLayer2(6), weightsLayer2(7), weightsLayer2(8), weightsLayer2(9), weightsLayer2(10)
200 PRINT "Layer 3 Weights: ", weightsLayer3(0), weightsLayer3(1), weightsLayer3(2), weightsLayer3(3), weightsLayer3(4), weightsLayer3(5), weightsLayer3(6), weightsLayer3(7), weightsLayer3(8), weightsLayer3(9), weightsLayer3(10)

210 REM Quantization Methods
220 REM Implement the quantization methods to optimize model size and accuracy
230 REM Pick your desired quantization method, either mean-based quantization or clustering-based quantization
240 REM For simplicity, we'll demonstrate mean-based quantization.

250 REM Mean-based Quantization - Reduce the number of bits used for the weights while preserving model accuracy
260 PRINT
270 PRINT "Quantization Results:"
280 PRINT

290 REM Set the desired number of bits for quantization (reduce the number of bits from original 64-bit representation)
300 DIM quntzbits(1)
310 quntzbits = 4

320 REM Calculate the quantization step size
330 DIM quntzstep(1)
340 quntzstep = (2 ^ quntzbits) - 1

350 REM Quantize and store the quantized weights
360 DIM quantizedWeightsLayer1(10), quantizedWeightsLayer2(10), quantizedWeightsLayer3(10)
370 FOR i = 0 TO 10
380 quantizedWeightsLayer1(i) = INT(weightsLayer1(i) * quntzstep) / quntzstep
390 quantizedWeightsLayer2(i) = INT(weightsLayer2(i) * quntzstep) / quntzstep
400 quantizedWeightsLayer3(i) = INT(weightsLayer3(i) * quntzstep) / quntzstep
410 NEXT i

420 PRINT "Quantized Model Weights:"
430 PRINT

440 PRINT "Layer 1 Weights: ", quantizedWeightsLayer1(0), quantizedWeightsLayer1(1), quantizedWeightsLayer1(2), quantizedWeightsLayer1(3), quantizedWeightsLayer1(4), quantizedWeightsLayer1(5), quantizedWeightsLayer1(6), quantizedWeightsLayer1(7), quantizedWeightsLayer1(8), quantizedWeightsLayer1(9), quantizedWeightsLayer1(10)

450 PRINT "Layer 2 Weights: ", quantizedWeightsLayer2(0), quantizedWeightsLayer2(1), quantizedWeightsLayer2(2), quantizedWeightsLayer2(3), quantizedWeightsLayer2(4), quantizedWeightsLayer2(5), quantizedWeightsLayer2(6), quantizedWeightsLayer2(7), quantizedWeightsLayer2(8), quantizedWeightsLayer2(9), quantizedWeightsLayer2(10)

460 PRINT "Layer 3 Weights: ", quantizedWeightsLayer3(0), quantizedWeightsLayer3(1), quantizedWeightsLayer3(2), quantizedWeightsLayer3(3), quantizedWeightsLayer3(4), quantizedWeightsLayer3(5), quantizedWeightsLayer3(6), quantizedWeightsLayer3(7), quantizedWeightsLayer3(8), quantizedWeightsLayer3(9), quantizedWeightsLayer3(10)
470 REM Further process the quantized weights if required, such as compressing them using advanced techniques, storing them in a more efficient format, etc.
480 REM Now, you can use the quantized weights for inference or further optimizations!

490 END