alu/tra-analysis
1
0
Fork 0
mirror of https://github.com/titanscouting/tra-analysis.git synced 2024-11-10 06:54:44 +00:00
Code Issues Packages Projects Releases Wiki Activity

analysis.py v 2.2.2

Browse Source 
Signed-off-by: Arthur Lu <learthurgo@gmail.com>
This commit is contained in:
Arthur Lu 2020-09-17 02:11:44 +00:00
parent 819245dfe2
commit 00605afcd7
1 changed files with 13 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

23
analysis-master/tra_analysis/analysis.py
View File

@ -7,12 +7,15 @@
# current benchmark of optimization: 1.33 times faster # current benchmark of optimization: 1.33 times faster
# setup: # setup:
__version__ = "2.2.1" __version__ = "2.2.2"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
2.2.2:
- fixed 2.2.1 changelog entry
- changed regression to return dictionary
2.2.1: 2.2.1:
changed all references to parent package analysis to tra_analysis - changed all references to parent package analysis to tra_analysis
2.2.0: 2.2.0:
- added Sort class - added Sort class
- added several array sorting functions to Sort class including: - added several array sorting functions to Sort class including:
@ -424,7 +427,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
X = np.array(inputs) X = np.array(inputs)
y = np.array(outputs) y = np.array(outputs)
regressions = [] regressions = {}
if 'lin' in args: # formula: ax + b if 'lin' in args: # formula: ax + b
@ -437,7 +440,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
popt, pcov = scipy.optimize.curve_fit(lin, X, y) popt, pcov = scipy.optimize.curve_fit(lin, X, y)
coeffs = popt.flatten().tolist() coeffs = popt.flatten().tolist()
regressions.append(str(coeffs[0]) + "*x+" + str(coeffs[1])) regressions["lin"] = (str(coeffs[0]) + "*x+" + str(coeffs[1]))
except Exception as e: except Exception as e:
@ -454,7 +457,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
popt, pcov = scipy.optimize.curve_fit(log, X, y) popt, pcov = scipy.optimize.curve_fit(log, X, y)
coeffs = popt.flatten().tolist() coeffs = popt.flatten().tolist()
regressions.append(str(coeffs[0]) + "*log(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3])) regressions["log"] = (str(coeffs[0]) + "*log(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
except Exception as e: except Exception as e:
@ -471,7 +474,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
popt, pcov = scipy.optimize.curve_fit(exp, X, y) popt, pcov = scipy.optimize.curve_fit(exp, X, y)
coeffs = popt.flatten().tolist() coeffs = popt.flatten().tolist()
regressions.append(str(coeffs[0]) + "*e^(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3])) regressions["exp"] = (str(coeffs[0]) + "*e^(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
except Exception as e: except Exception as e:
@ -482,7 +485,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
inputs = np.array([inputs]) inputs = np.array([inputs])
outputs = np.array([outputs]) outputs = np.array([outputs])
plys = [] plys = {}
limit = len(outputs[0]) limit = len(outputs[0])
for i in range(2, limit): for i in range(2, limit):
@ -500,9 +503,9 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
for param in params: for param in params:
temp += "(" + str(param) + "*x^" + str(counter) + ")" temp += "(" + str(param) + "*x^" + str(counter) + ")"
counter += 1 counter += 1
plys.append(temp) plys["x^" + str(i)] = (temp)
regressions.append(plys) regressions["ply"] = (plys)
if 'sig' in args: # formula: a tanh (b(x + c)) + d if 'sig' in args: # formula: a tanh (b(x + c)) + d
@ -515,7 +518,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
popt, pcov = scipy.optimize.curve_fit(sig, X, y) popt, pcov = scipy.optimize.curve_fit(sig, X, y)
coeffs = popt.flatten().tolist() coeffs = popt.flatten().tolist()
regressions.append(str(coeffs[0]) + "*tanh(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3])) regressions["sig"] = (str(coeffs[0]) + "*tanh(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
except Exception as e: except Exception as e: