analysis.py v 1.1.13.001

analysis pkg v 1.0.0.006

analysis-master/analysis.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: analysis
-Version: 1.0.0.5
+Version: 1.0.0.6
 Summary: analysis package developed by Titan Scouting for The Red Alliance
 Home-page: https://github.com/titanscout2022/tr2022-strategy
 Author: The Titan Scouting Team

analysis-master/analysis/analysis.py

@@ -7,10 +7,14 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.13.000"
+__version__ = "1.1.13.001"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    1.1.13.001:
+        - bug fix with linear regression not returning a proper value
+        - cleaned up regression
+        - fixed bug with polynomial regressions
     1.1.13.000:
         - fixed all regressions to now properly work
     1.1.12.006:
@@ -343,28 +347,27 @@ def histo_analysis(hist_data):
 
         return None
 
-def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01, _iterations_ply = 10000, lr_ply = 0.01): # inputs, outputs expects N-D array 
+def regression(inputs, outputs, args): # inputs, outputs expects N-D array 
 
     regressions = []
-    Regression().set_device(ndevice)
 
     if 'lin' in args: # formula: ax + b
 
         try:
 
-            X = np.array(inputs).reshape(-1,1)
+            X = np.array(inputs)
             y = np.array(outputs)
 
-            model = sklearn.linear_model.LinearRegression().fit(X, y)
+            def func(x, a, b):
 
-            ret = model.coef_.flatten().tolist()
-            ret.append(model.intercept_)
+                return a * x + b
 
-            regressions.append((ret, model.score(X,y)))
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
 
         except Exception as e:
 
-            print(e)
             pass
 
     if 'log' in args: # formula: a log (b(x + c)) + d
@@ -384,7 +387,6 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
 
         except Exception as e:
             
-            print(e)
             pass
 
     if 'exp' in args: # formula: a e ^ (b(x + c)) + d
@@ -404,11 +406,13 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
 
         except Exception as e:
 
-            print(e)
             pass
 
     if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
         
+        inputs = [inputs]
+        outputs = [outputs]
+
         plys = []
         limit = len(outputs[0])
 
@@ -444,7 +448,6 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
 
         except Exception as e:
            
-            print(e)
             pass
 
     return regressions

analysis-master/build/lib/analysis/analysis.py

@@ -7,10 +7,14 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.13.000"
+__version__ = "1.1.13.001"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    1.1.13.001:
+        - bug fix with linear regression not returning a proper value
+        - cleaned up regression
+        - fixed bug with polynomial regressions
     1.1.13.000:
         - fixed all regressions to now properly work
     1.1.12.006:
@@ -343,28 +347,27 @@ def histo_analysis(hist_data):
 
         return None
 
-def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01, _iterations_ply = 10000, lr_ply = 0.01): # inputs, outputs expects N-D array 
+def regression(inputs, outputs, args): # inputs, outputs expects N-D array 
 
     regressions = []
-    Regression().set_device(ndevice)
 
     if 'lin' in args: # formula: ax + b
 
         try:
 
-            X = np.array(inputs).reshape(-1,1)
+            X = np.array(inputs)
             y = np.array(outputs)
 
-            model = sklearn.linear_model.LinearRegression().fit(X, y)
+            def func(x, a, b):
 
-            ret = model.coef_.flatten().tolist()
-            ret.append(model.intercept_)
+                return a * x + b
 
-            regressions.append((ret, model.score(X,y)))
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
 
         except Exception as e:
 
-            print(e)
             pass
 
     if 'log' in args: # formula: a log (b(x + c)) + d
@@ -384,7 +387,6 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
 
         except Exception as e:
             
-            print(e)
             pass
 
     if 'exp' in args: # formula: a e ^ (b(x + c)) + d
@@ -404,11 +406,13 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
 
         except Exception as e:
 
-            print(e)
             pass
 
     if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
         
+        inputs = [inputs]
+        outputs = [outputs]
+
         plys = []
         limit = len(outputs[0])
 
@@ -444,7 +448,6 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
 
         except Exception as e:
            
-            print(e)
             pass
 
     return regressions

analysis-master/setup.py

@@ -2,7 +2,7 @@ import setuptools
 
 setuptools.setup(
     name="analysis", # Replace with your own username
-    version="1.0.0.005",
+    version="1.0.0.006",
     author="The Titan Scouting Team",
     author_email="titanscout2022@gmail.com",
     description="analysis package developed by Titan Scouting for The Red Alliance",

data analysis/superscript.py

@@ -70,8 +70,10 @@ __all__ = [
 from analysis import analysis as an
 import data as d
 import time
+import warnings
 
 def main():
+    warnings.filterwarnings("ignore")
     while(True):
 
         current_time = time.time()
@@ -152,19 +154,19 @@ def simplestats(data, test):
         return an.histo_analysis([list(range(len(data))), data])
 
     if(test == "regression_linear"):
-        return an.regression('cpu', [list(range(len(data)))], [data], ['lin'], _iterations = 5000)
+        return an.regression(list(range(len(data))), data, ['lin'])
 
     if(test == "regression_logarithmic"):
-        return an.regression('cpu', [list(range(len(data)))], [data], ['log'], _iterations = 5000)
+        return an.regression(list(range(len(data))), data, ['log'])
 
     if(test == "regression_exponential"):
-        return an.regression('cpu', [list(range(len(data)))], [data], ['exp'], _iterations = 5000)
+        return an.regression(list(range(len(data))), data, ['exp'])
 
     if(test == "regression_polynomial"):
-        return an.regression('cpu', [list(range(len(data)))], [data], ['ply'], _iterations = 5000)
+        return an.regression(list(range(len(data))), data, ['ply'])
 
     if(test == "regression_sigmoidal"):
-        return an.regression('cpu', [list(range(len(data)))], [data], ['sig'], _iterations = 5000)
+        return an.regression(list(range(len(data))), data, ['sig'])
 
 def push_to_database(apikey, competition, results, metrics):