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analysis.py v 1.1.13.001

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analysis pkg v 1.0.0.006
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art
2020-03-05 13:18:33 -06:00
parent e488b4a4d1
commit d0dc9a1047
9 changed files with 43 additions and 35 deletions
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analysis-master/analysis/analysis.py
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@@ -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
@@ -383,8 +386,7 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
regressions.append((popt.flatten().tolist(), None))
except Exception as e:
print(e)
pass
if 'exp' in args: # formula: a e ^ (b(x + c)) + d
@@ -404,10 +406,12 @@ 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])
@@ -443,8 +447,7 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
regressions.append((popt.flatten().tolist(), None))
except Exception as e:
print(e)
pass
return regressions