v9.0

AI Tests

Author: MitchellShibilski-Unkel

AI/__pycache__/aiScoring.cpython-312.pyc

@@ -0,0 +1,28 @@
+import time
+from AI.aiTests import *
+
+
+def score(_in, device, test: int = 1, inputSize: int = 16, hx: int = 32, outputSize: int = 1, numLayers: int = 24, model = "google/gemma-3-270m", type: str = "text-generation"):
+    startTimer = time.perf_counter()
+    startTimer2 = time.process_time()
+    
+    change = 0 # Placeholder for HuggingFace
+    
+    if test == 1:
+        SimpleNeuralNet(inputSize, hx, outputSize).to(device)(_in)
+    elif test == 2:
+        RNN(inputSize, hx, outputSize, numLayers).to(device)(_in)
+    elif test == 3:
+        LargeNeuralNet(inputSize, hx, outputSize, numLayers).to(device)(_in)
+    elif test == 4:
+        HuggingFace(model, device, type).model(_in)
+        change = 100
+    else:
+        SimpleNeuralNet(inputSize, hx, outputSize).to(device)(_in)
+        
+    endTimer = time.perf_counter()
+    endTimer2 = time.process_time()
+    
+    totalTime = (endTimer - startTimer) + (endTimer2 - startTimer2)
+    points = (1000 // totalTime * 0.1) + change
+    return f"AI Score: {points} | Time Taken: {totalTime}s"

AI/__pycache__/aiTests.cpython-312.pyc

@@ -0,0 +1,73 @@
+from torch import nn
+
+
+class SimpleNeuralNet(nn.Module):
+    def __init__(self, inputSize: int = 16, hx: int = 32, outputSize: int = 1):
+        super(SimpleNeuralNet, self).__init__()
+        self.inputSize = inputSize
+        self.hx = hx
+        self.outputSize = outputSize
+        
+    def forward(self, x):
+        net = nn.Sequential(
+            nn.Linear(self.inputSize, self.hx),
+            nn.ReLU(),
+            nn.Linear(self.hx, self.outputSize),
+            nn.Sigmoid()
+        )
+        
+        return net
+    
+class RNN(nn.Module):
+    def __init__(self, inputSize: int = 16, hx: int = 32, outputSize: int = 1, numLayers: int = 24):
+        super(RNN, self).__init__()
+        self.inputSize = inputSize
+        self.hx = hx
+        self.outputSize = outputSize
+        self.numLayers = numLayers
+        
+    def forward(self, x):
+        rnn = nn.Sequential(
+            nn.RNN(self.inputSize, self.hx, self.numLayers),
+            nn.Linear(self.inputSize, self.hx),
+            nn.ReLU(),
+            nn.Linear(self.hx, self.outputSize),
+            nn.Sigmoid()
+        )
+        
+        return rnn
+    
+class LargeNeuralNet(nn.Module):
+    def __init__(self, inputSize: int = 16, hx: int = 32, outputSize: int = 1):
+        super(LargeNeuralNet, self).__init__()
+        self.inputSize = inputSize
+        self.hx = hx
+        self.outputSize = outputSize
+        
+    def forward(self, x):
+        net = nn.Sequential(
+            nn.LSTM(self.inputSize, self.hx, self.outputSize, bidirectional=True),
+            nn.Linear(self.inputSize, self.hx),
+            nn.ReLU(),
+            nn.Linear(self.hx, self.outputSize),
+            nn.ReLU(),
+            nn.Linear(self.hx, self.outputSize),
+            nn.ReLU(),
+            nn.Linear(self.hx, self.outputSize),
+            nn.ReLU(),
+            nn.Linear(self.hx, self.outputSize),
+            nn.Sigmoid()
+        )
+        
+        return net
+    
+class HuggingFace:
+    def __init__(self, modelName: str = "google/gemma-3-270m", device: str = "cpu", type: str = "text-generation"):
+        self.modelName = modelName
+        self.device = device
+        self.type = type
+        
+    def model(self, data):
+        from transformers import pipeline
+        pipe = pipeline(self.type, self.modelName, device=self.device)(data)
+        return pipe

AI/aiScoring.py

@@ -1,6 +1,5 @@
 from Algorthmic_Tests import primeCounter
 from Algorthmic_Tests import waveFunction
-from Algorthmic_Tests import rnnAlgorithm
 import time