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ultra galaxybrain working
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@ -7,10 +7,12 @@
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# current benchmark of optimization: 1.33 times faster
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# current benchmark of optimization: 1.33 times faster
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# setup:
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# setup:
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__version__ = "1.1.13.003"
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__version__ = "1.1.13.004"
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# changelog should be viewed using print(analysis.__changelog__)
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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__changelog__ = """changelog:
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1.1.13.004:
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- small fixes to regression to improve performance
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1.1.13.003:
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1.1.13.003:
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- filtered nans from regression
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- filtered nans from regression
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1.1.13.002:
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1.1.13.002:
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@ -348,11 +350,9 @@ def histo_analysis(hist_data):
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def regression(inputs, outputs, args): # 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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inputs = np.array(inputs)
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X = np.array(inputs)
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outputs = np.array(outputs)
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y = np.array(outputs)
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inputs = inputs[np.isfinite(inputs)]
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outputs = outputs[np.isfinite(outputs)]
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regressions = []
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regressions = []
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if 'lin' in args: # formula: ax + b
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if 'lin' in args: # formula: ax + b
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@ -3,10 +3,13 @@
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# Notes:
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# Notes:
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# setup:
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# setup:
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__version__ = "0.0.4.000"
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__version__ = "0.0.4.001"
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# changelog should be viewed using print(analysis.__changelog__)
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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__changelog__ = """changelog:
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0.0.4.001:
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- fixed bug where X range for regression was determined before sanitization
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- better sanitized data
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0.0.4.000:
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0.0.4.000:
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- fixed spelling issue in __changelog__
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- fixed spelling issue in __changelog__
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- addressed nan bug in regression
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- addressed nan bug in regression
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@ -76,6 +79,7 @@ __all__ = [
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from analysis import analysis as an
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from analysis import analysis as an
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import data as d
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import data as d
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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import time
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import time
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import warnings
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import warnings
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@ -150,6 +154,8 @@ def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
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variable_data = data[team][variable]
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variable_data = data[team][variable]
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if(variable in tests):
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if(variable in tests):
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for test in tests[variable]:
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for test in tests[variable]:
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print(team)
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print(variable)
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test_vector[test] = simplestats(variable_data, test)
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test_vector[test] = simplestats(variable_data, test)
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else:
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else:
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pass
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pass
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@ -160,26 +166,30 @@ def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
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def simplestats(data, test):
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def simplestats(data, test):
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data = np.array(data)
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data = data[np.isfinite(data)]
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ranges = list(range(len(data)))
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if(test == "basic_stats"):
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if(test == "basic_stats"):
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return an.basic_stats(data)
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return an.basic_stats(data)
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if(test == "historical_analysis"):
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if(test == "historical_analysis"):
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return an.histo_analysis([list(range(len(data))), data])
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return an.histo_analysis([ranges, data])
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if(test == "regression_linear"):
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if(test == "regression_linear"):
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return an.regression(list(range(len(data))), data, ['lin'])
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return an.regression(ranges, data, ['lin'])
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if(test == "regression_logarithmic"):
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if(test == "regression_logarithmic"):
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return an.regression(list(range(len(data))), data, ['log'])
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return an.regression(ranges, data, ['log'])
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if(test == "regression_exponential"):
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if(test == "regression_exponential"):
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return an.regression(list(range(len(data))), data, ['exp'])
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return an.regression(ranges, data, ['exp'])
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if(test == "regression_polynomial"):
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if(test == "regression_polynomial"):
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return an.regression(list(range(len(data))), data, ['ply'])
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return an.regression(ranges, data, ['ply'])
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if(test == "regression_sigmoidal"):
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if(test == "regression_sigmoidal"):
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return an.regression(list(range(len(data))), data, ['sig'])
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return an.regression(ranges, data, ['sig'])
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def push_to_database(apikey, competition, results, pit):
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def push_to_database(apikey, competition, results, pit):
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