titanlearn.py v 2.0.0.000

data analysis/analysis/analysis.py

@@ -1,10 +1,10 @@
 # Titan Robotics Team 2022: Data Analysis Module
 # Written by Arthur Lu & Jacob Levine
 # Notes:
-#   this should be imported as a python module using 'import analysis'
-#   this should be included in the local directory or environment variable
-#   this module has been optimized for multhreaded computing
-#   current benchmark of optimization: 1.33 times faster
+#    this should be imported as a python module using 'import analysis'
+#    this should be included in the local directory or environment variable
+#    this module has been optimized for multhreaded computing
+#    current benchmark of optimization: 1.33 times faster
 # setup:
 
 __version__ = "1.1.5.001"

data analysis/analysis/regression.py

@@ -1,9 +1,9 @@
 # Titan Robotics Team 2022: CUDA-based Regressions Module
 # Written by Arthur Lu & Jacob Levine
 # Notes:
-#   this should be imported as a python module using 'import regression'
-#   this should be included in the local directory or environment variable
-#   this module is cuda-optimized and vectorized (except for one small part)
+#    this should be imported as a python module using 'import regression'
+#    this should be included in the local directory or environment variable
+#    this module is cuda-optimized and vectorized (except for one small part)
 # setup:
 
 __version__ = "1.0.0.002"

data analysis/analysis/titanlearn.py

@@ -0,0 +1,52 @@
+# Titan Robotics Team 2022: ML Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#    this should be imported as a python module using 'import titanlearn'
+#    this should be included in the local directory or environment variable
+#    this module is optimized for multhreaded computing
+#    this module learns from its mistakes far faster than 2022's captains
+# setup:
+
+__version__ = "2.0.0.000"
+
+#changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+2.0.0.000:
+    - complete rewrite planned
+    - depreciated 1.0.0.xxx versions
+    - added simple training loop
+1.0.0.xxx:
+    -added generation of ANNS, basic SGD training
+"""
+
+__author__ = (
+    "Arthur Lu <arthurlu@ttic.edu>, "
+    "Jacob Levine <jlevine@ttic.edu>,"
+    )
+
+__all__ = [
+    'train',
+    ]
+
+import torch
+import torch.optim as optim
+
+def train(device, net, epochs, trainloader, optimizer, criterion):
+
+    for epoch in range(epochs):  # loop over the dataset multiple times
+
+        for i, data in enumerate(trainloader, 0):
+
+            inputs = data[0].to(device)
+            labels = data[1].to(device)
+
+            optimizer.zero_grad()
+
+            outputs = net(inputs)
+            loss = criterion(outputs, labels.to(torch.float))
+            
+            loss.backward()
+            optimizer.step()
+        
+    return net
+    print("finished training")