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analysis.py v 1.1.13.001
analysis pkg v 1.0.0.006
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Metadata-Version: 2.1
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Name: analysis
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Version: 1.0.0.5
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Version: 1.0.0.6
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Summary: analysis package developed by Titan Scouting for The Red Alliance
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Home-page: https://github.com/titanscout2022/tr2022-strategy
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Author: The Titan Scouting Team
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# current benchmark of optimization: 1.33 times faster
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# setup:
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__version__ = "1.1.13.000"
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__version__ = "1.1.13.001"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.1.13.001:
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- bug fix with linear regression not returning a proper value
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- cleaned up regression
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- fixed bug with polynomial regressions
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1.1.13.000:
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- fixed all regressions to now properly work
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1.1.12.006:
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@ -343,28 +347,27 @@ def histo_analysis(hist_data):
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return None
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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
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def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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regressions = []
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Regression().set_device(ndevice)
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if 'lin' in args: # formula: ax + b
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try:
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X = np.array(inputs).reshape(-1,1)
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X = np.array(inputs)
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y = np.array(outputs)
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model = sklearn.linear_model.LinearRegression().fit(X, y)
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def func(x, a, b):
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ret = model.coef_.flatten().tolist()
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ret.append(model.intercept_)
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return a * x + b
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regressions.append((ret, model.score(X,y)))
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popt, pcov = scipy.optimize.curve_fit(func, X, y)
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regressions.append((popt.flatten().tolist(), None))
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except Exception as e:
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print(e)
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pass
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if 'log' in args: # formula: a log (b(x + c)) + d
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@ -384,7 +387,6 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
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except Exception as e:
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print(e)
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pass
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if 'exp' in args: # formula: a e ^ (b(x + c)) + d
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@ -404,11 +406,13 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
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except Exception as e:
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print(e)
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pass
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if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
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inputs = [inputs]
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outputs = [outputs]
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plys = []
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limit = len(outputs[0])
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@ -444,7 +448,6 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
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except Exception as e:
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print(e)
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pass
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return regressions
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@ -7,10 +7,14 @@
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# current benchmark of optimization: 1.33 times faster
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# setup:
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__version__ = "1.1.13.000"
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__version__ = "1.1.13.001"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.1.13.001:
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- bug fix with linear regression not returning a proper value
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- cleaned up regression
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- fixed bug with polynomial regressions
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1.1.13.000:
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- fixed all regressions to now properly work
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1.1.12.006:
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@ -343,28 +347,27 @@ def histo_analysis(hist_data):
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return None
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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
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def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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regressions = []
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Regression().set_device(ndevice)
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if 'lin' in args: # formula: ax + b
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try:
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X = np.array(inputs).reshape(-1,1)
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X = np.array(inputs)
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y = np.array(outputs)
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model = sklearn.linear_model.LinearRegression().fit(X, y)
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def func(x, a, b):
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ret = model.coef_.flatten().tolist()
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ret.append(model.intercept_)
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return a * x + b
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regressions.append((ret, model.score(X,y)))
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popt, pcov = scipy.optimize.curve_fit(func, X, y)
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regressions.append((popt.flatten().tolist(), None))
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except Exception as e:
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print(e)
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pass
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if 'log' in args: # formula: a log (b(x + c)) + d
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@ -384,7 +387,6 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
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except Exception as e:
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print(e)
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pass
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if 'exp' in args: # formula: a e ^ (b(x + c)) + d
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@ -404,11 +406,13 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
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except Exception as e:
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print(e)
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pass
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if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
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inputs = [inputs]
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outputs = [outputs]
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plys = []
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limit = len(outputs[0])
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@ -444,7 +448,6 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
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except Exception as e:
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print(e)
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pass
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return regressions
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analysis-master/dist/analysis-1.0.0.5.tar.gz
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analysis-master/dist/analysis-1.0.0.6.tar.gz
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@ -2,7 +2,7 @@ import setuptools
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setuptools.setup(
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name="analysis", # Replace with your own username
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version="1.0.0.005",
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version="1.0.0.006",
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author="The Titan Scouting Team",
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author_email="titanscout2022@gmail.com",
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description="analysis package developed by Titan Scouting for The Red Alliance",
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@ -70,8 +70,10 @@ __all__ = [
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from analysis import analysis as an
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import data as d
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import time
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import warnings
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def main():
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warnings.filterwarnings("ignore")
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while(True):
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current_time = time.time()
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return an.histo_analysis([list(range(len(data))), data])
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if(test == "regression_linear"):
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return an.regression('cpu', [list(range(len(data)))], [data], ['lin'], _iterations = 5000)
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return an.regression(list(range(len(data))), data, ['lin'])
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if(test == "regression_logarithmic"):
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return an.regression('cpu', [list(range(len(data)))], [data], ['log'], _iterations = 5000)
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return an.regression(list(range(len(data))), data, ['log'])
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if(test == "regression_exponential"):
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return an.regression('cpu', [list(range(len(data)))], [data], ['exp'], _iterations = 5000)
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return an.regression(list(range(len(data))), data, ['exp'])
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if(test == "regression_polynomial"):
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return an.regression('cpu', [list(range(len(data)))], [data], ['ply'], _iterations = 5000)
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return an.regression(list(range(len(data))), data, ['ply'])
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if(test == "regression_sigmoidal"):
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return an.regression('cpu', [list(range(len(data)))], [data], ['sig'], _iterations = 5000)
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return an.regression(list(range(len(data))), data, ['sig'])
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def push_to_database(apikey, competition, results, metrics):
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