mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-11-09 22:44:44 +00:00
analysis.py v 2.2.2
Signed-off-by: Arthur Lu <learthurgo@gmail.com>
This commit is contained in:
parent
819245dfe2
commit
00605afcd7
@ -7,12 +7,15 @@
|
||||
# current benchmark of optimization: 1.33 times faster
|
||||
# setup:
|
||||
|
||||
__version__ = "2.2.1"
|
||||
__version__ = "2.2.2"
|
||||
|
||||
# changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
2.2.2:
|
||||
- fixed 2.2.1 changelog entry
|
||||
- changed regression to return dictionary
|
||||
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:
|
||||
- added Sort class
|
||||
- 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)
|
||||
y = np.array(outputs)
|
||||
|
||||
regressions = []
|
||||
regressions = {}
|
||||
|
||||
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)
|
||||
|
||||
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:
|
||||
|
||||
@ -454,7 +457,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
|
||||
popt, pcov = scipy.optimize.curve_fit(log, X, y)
|
||||
|
||||
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:
|
||||
|
||||
@ -471,7 +474,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
|
||||
popt, pcov = scipy.optimize.curve_fit(exp, X, y)
|
||||
|
||||
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:
|
||||
|
||||
@ -482,7 +485,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
|
||||
inputs = np.array([inputs])
|
||||
outputs = np.array([outputs])
|
||||
|
||||
plys = []
|
||||
plys = {}
|
||||
limit = len(outputs[0])
|
||||
|
||||
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:
|
||||
temp += "(" + str(param) + "*x^" + str(counter) + ")"
|
||||
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
|
||||
|
||||
@ -515,7 +518,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
|
||||
popt, pcov = scipy.optimize.curve_fit(sig, X, y)
|
||||
|
||||
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:
|
||||
|
||||
|
Loading…
Reference in New Issue
Block a user