def Solve(self, inUserFunctionString = None, inAlgorithmName="Levenberg-Marquardt"):
if inUserFunctionString:
self.ParseAndCompileUserFunctionString(inUserFunctionString)
# starting point
if len(self.estimatedCoefficients) == 0:
self.estimatedCoefficients = pyeq2.solverService().SolveUsingDE(self)
if self.fittingTarget == 'ODR':
return pyeq2.solverService().SolveUsingODR(self)
self.estimatedCoefficients = pyeq2.solverService().SolveUsingSelectedAlgorithm(self, inAlgorithmName=inAlgorithmName)
return pyeq2.solverService().SolveUsingSimplex(self)
def Solve(self, inUserFunctionString = None, inAlgorithmName="Levenberg-Marquardt"):
if inUserFunctionString:
self.ParseAndCompileUserFunctionString(inUserFunctionString)
# starting point
if len(self.estimatedCoefficients) == 0:
self.estimatedCoefficients = pyeq2.solverService().SolveUsingDE(self)
if self.fittingTarget == 'ODR':
return pyeq2.solverService().SolveUsingODR(self)
self.estimatedCoefficients = pyeq2.solverService().SolveUsingSelectedAlgorithm(self, inAlgorithmName=inAlgorithmName)
return pyeq2.solverService().SolveUsingSimplex(self)
def scaleFitter(fit_eq, file, estimated,weight):
'''Ajusta datos del archivo para mi función arbitraria
que es un escalamiento de la función Membrane Transport'''
f=open(file)
data = f.read()
#Crea el objeto de función a escala y lo llena de datos
equation = pyeq2.Models_2D.UserDefinedFunction.UserDefinedFunction('SSQABS','Default', fit_eq)
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(data, equation,weight)
f.close()
#Calcula el ajuste
equation.estimatedCoefficients = estimated
pyeq2.solverService().SolveUsingSimplex(equation)
equation.CalculateCoefficientAndFitStatistics()
return equation
def test_SolveUsingSimplex_3D(self):
coefficientsShouldBe = numpy.array([0.28658383, -0.90215775, 1.15483864])
model = pyeq2.Models_3D.Polynomial.Linear('SSQABS')
model.estimatedCoefficients = numpy.array([1.0, 1.0, 1.0]) # starting values for the simplex solver
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_3D, model, False)
coefficients = pyeq2.solverService().SolveUsingSimplex(model)
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0E-06, atol=1.0E-300))
def test_SolveUsingODR_3D(self):
coefficientsShouldBe = numpy.array([-0.04925, -0.90509, 1.28076])
model = pyeq2.Models_3D.Polynomial.Linear('ODR')
model.estimatedCoefficients = numpy.array([0.2, -1.0, 1.0]) # starting values for the ODR solver
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_3D, model, False)
coefficients = pyeq2.solverService().SolveUsingODR(model)
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0E-03, atol=1.0E-300))
def test_SolveUsingSimplex_3D(self):
coefficientsShouldBe = numpy.array([0.28658383, -0.90215775, 1.15483864])
model = pyeq2.Models_3D.Polynomial.Linear("SSQABS")
model.estimatedCoefficients = numpy.array([1.0, 1.0, 1.0]) # starting values for the simplex solver
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_3D, model, False)
coefficients = pyeq2.solverService().SolveUsingSimplex(model)
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0e-06, atol=1.0e-300))
def test_SolveUsingSimplex_SSQREL_2D(self):
coefficientsShouldBe = numpy.array([-6.74510573, 1.32459622])
model = pyeq2.Models_2D.Polynomial.Linear('SSQREL')
model.estimatedCoefficients = numpy.array([1.0, 1.0]) # starting values for the simplex solver
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_2D, model, False)
coefficients = pyeq2.solverService().SolveUsingSimplex(model)
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0E-06, atol=1.0E-300))
def test_SolveUsingSpline_2D(self):
knotPointsShouldBe = numpy.array(
[5.357, 5.357, 5.357, 5.357, 9.861, 9.861, 9.861, 9.861])
coefficientsShouldBe = numpy.array([
0.38297001, 1.95535226, 4.59605664, 7.16162379, 0.0, 0.0, 0.0, 0.0
])
testEvaluationShouldBe = numpy.array([4.02361487093])
model = pyeq2.Models_2D.Spline.Spline(inSmoothingFactor=1.0,
inXOrder=3)
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(
DataForUnitTests.asciiDataInColumns_2D, model, False)
fittedParameters = pyeq2.solverService().SolveUsingSpline(model)
# example of later using the saved spline knot points and coefficients
unFittedSpline = scipy.interpolate.fitpack2.UnivariateSpline(
model.dataCache.allDataCacheDictionary['X'],
model.dataCache.allDataCacheDictionary['DependentData'],
s=model.smoothingFactor,
k=model.xOrder)
unFittedSpline._eval_args = fittedParameters
testEvaluation = unFittedSpline(numpy.array([8.0]))
self.assertTrue(
numpy.allclose(testEvaluation,
testEvaluationShouldBe,
rtol=1.0E-10,
atol=1.0E-300))
self.assertTrue(
numpy.equal(fittedParameters[0], knotPointsShouldBe).all())
self.assertTrue(
numpy.allclose(fittedParameters[1],
coefficientsShouldBe,
rtol=1.0E-06,
atol=1.0E-300))
def test_SolveUsingLevenbergMarquardt_2D(self):
coefficientsShouldBe = numpy.array([-8.01913565, 1.5264473])
model = pyeq2.Models_2D.Polynomial.Linear('SSQABS')
model.estimatedCoefficients = numpy.array([-4.0, 2.0]) # starting values for the simplex solver
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_2D, model, False)
coefficients = pyeq2.solverService().SolveUsingSelectedAlgorithm(model, inAlgorithmName="Levenberg-Marquardt")
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0E-06, atol=1.0E-300))
def test_SolveUsingSimplex_SSQREL_2D(self):
coefficientsShouldBe = numpy.array([-6.74510573, 1.32459622])
model = pyeq2.Models_2D.Polynomial.Linear("SSQREL")
model.estimatedCoefficients = numpy.array([1.0, 1.0]) # starting values for the simplex solver
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_2D, model, False)
coefficients = pyeq2.solverService().SolveUsingSimplex(model)
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0e-06, atol=1.0e-300))
def Solve(self):
solver = pyeq2.solverService()
if self.splineFlag:
return solver.SolveUsingSpline(self)
if self.fixedCoefficients != []:
self._canLinearSolverBeUsedForSSQABS = False
if self.fittingTarget == 'SSQABS':
if self.CanLinearSolverBeUsedForSSQABS() == True:
return solver.SolveUsingLinear(self)
else:
self.estimatedCoefficients = solver.SolveUsingDE(self)
self.estimatedCoefficients = solver.SolveUsingLevenbergMarquardt(
self)
return solver.SolveUsingSimplex(self)
if self.fittingTarget == 'ODR':
self.estimatedCoefficients = solver.SolveUsingDE(self)
return solver.SolveUsingODR(self)
# default
self.estimatedCoefficients = solver.SolveUsingDE(self)
return solver.SolveUsingSimplex(self)
def test_SolveUsingODR_3D(self):
coefficientsShouldBe = numpy.array([-0.04925, -0.90509, 1.28076])
model = pyeq2.Models_3D.Polynomial.Linear("ODR")
model.estimatedCoefficients = numpy.array([0.2, -1.0, 1.0]) # starting values for the ODR solver
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_3D, model, False)
coefficients = pyeq2.solverService().SolveUsingODR(model)
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0e-03, atol=1.0e-300))
def test_SolveUsingDE_3D(self):
coefficientsShouldBe = numpy.array([-2.05105972, -0.49194959, 1.77817475])
model = pyeq2.Models_3D.UserDefinedFunction.UserDefinedFunction('SSQABS', 'Default', 'a + b*X + c*Y')
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_3D_small, model, False)
coefficients = pyeq2.solverService().SolveUsingDE(model)
fittingTarget = model.CalculateAllDataFittingTarget(coefficients)
self.assertTrue(fittingTarget <= 0.1)
def test_SolveUsingDE_3D(self):
coefficientsShouldBe = numpy.array([-2.05105972, -0.49194959, 1.77817475])
model = pyeq2.Models_3D.UserDefinedFunction.UserDefinedFunction('SSQABS', 'Default', 'a + b*X + c*Y')
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_3D_small, model, False)
coefficients = pyeq2.solverService().SolveUsingDE(model)
fittingTarget = model.CalculateAllDataFittingTarget(coefficients)
self.assertTrue(fittingTarget <= 0.1)
def test_SolveUsingLevenbergMarquardt_2D(self):
coefficientsShouldBe = numpy.array([-8.01913565, 1.5264473])
model = pyeq2.Models_2D.Polynomial.Linear("SSQABS")
model.estimatedCoefficients = numpy.array([-4.0, 2.0]) # starting values for the simplex solver
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_2D, model, False)
coefficients = pyeq2.solverService().SolveUsingLevenbergMarquardt(model)
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0e-06, atol=1.0e-300))
def test_SolveUsingSpline_3D(self):
xKnotPointsShouldBe = numpy.array([0.607, 0.607, 0.607, 3.017, 3.017, 3.017])
yKnotPointsShouldBe = numpy.array([1.984, 1.984, 1.984, 3.153, 3.153, 3.153])
coefficientsShouldBe = numpy.array(
[2.33418963, 1.80079612, 5.07902936, 0.54445029, 1.04110843, 2.14180324, 0.26992805, 0.39148852, 0.8177307]
)
testEvaluationShouldBe = numpy.array([0.76020577997])
model = pyeq2.Models_3D.Spline.Spline(inSmoothingFactor=1.0, inXOrder=2, inYOrder=2)
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_3D, model, False)
fittedParameters = pyeq2.solverService().SolveUsingSpline(model)
# example of later using the saved spline knot points and coefficients
unFittedSpline = scipy.interpolate.fitpack2.SmoothBivariateSpline(
model.dataCache.allDataCacheDictionary["X"],
model.dataCache.allDataCacheDictionary["Y"],
model.dataCache.allDataCacheDictionary["DependentData"],
s=model.smoothingFactor,
kx=model.xOrder,
ky=model.yOrder,
)
unFittedSpline.tck = fittedParameters
testEvaluation = unFittedSpline.ev(2.5, 2.5)
self.assertTrue(numpy.allclose(testEvaluation, testEvaluationShouldBe, rtol=1.0e-10, atol=1.0e-300))
self.assertTrue(numpy.equal(fittedParameters[0], xKnotPointsShouldBe).all())
self.assertTrue(numpy.equal(fittedParameters[1], yKnotPointsShouldBe).all())
self.assertTrue(numpy.allclose(fittedParameters[2], coefficientsShouldBe, rtol=1.0e-06, atol=1.0e-300))
def test_SolveUsingLevenbergMarquardt_3D(self):
coefficientsShouldBe = numpy.array([0.28658387, -0.90215776, 1.15483863])
model = pyeq2.Models_3D.Polynomial.Linear('SSQABS')
model.estimatedCoefficients = numpy.array([0.2, -1.0, 1.0]) # starting values for the simplex solver
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_3D, model, False)
coefficients = pyeq2.solverService().SolveUsingSelectedAlgorithm(model, inAlgorithmName="Levenberg-Marquardt")
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0E-05, atol=1.0E-300))
def scaleFitter(fit_eq, file, estimated, weight):
'''Ajusta datos del archivo para mi función arbitraria
que es un escalamiento de la función Membrane Transport'''
f = open(file)
data = f.read()
#Crea el objeto de función a escala y lo llena de datos
equation = pyeq2.Models_2D.UserDefinedFunction.UserDefinedFunction(
'SSQABS', 'Default', fit_eq)
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(
data, equation, weight)
f.close()
#Calcula el ajuste
equation.estimatedCoefficients = estimated
pyeq2.solverService().SolveUsingSimplex(equation)
equation.CalculateCoefficientAndFitStatistics()
return equation
def test_SolveUsingDE_2D(self):
coefficientsShouldBe = numpy.array([-6.15515504031, 1.21618173729])
model = pyeq2.Models_2D.UserDefinedFunction.UserDefinedFunction("SSQABS", "Default", "m*X + b")
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(
DataForUnitTests.asciiDataInColumns_2D_small, model, False
)
coefficients = pyeq2.solverService().SolveUsingDE(model)
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0e-06, atol=1.0e-300))
def test_SolveUsingDE_3D(self):
coefficientsShouldBe = numpy.array([-0.206068766376, -0.644872592849, 1.13361007134])
model = pyeq2.Models_3D.UserDefinedFunction.UserDefinedFunction("SSQABS", "Default", "a + b*X + c*Y")
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(
DataForUnitTests.asciiDataInColumns_3D_small, model, False
)
coefficients = pyeq2.solverService().SolveUsingDE(model)
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0e-06, atol=1.0e-300))
def test_SolveUsingODR_2D(self):
coefficientsShouldBe = numpy.array([-8.04624, 1.53032])
model = pyeq2.Models_2D.Polynomial.Linear('ODR')
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(
DataForUnitTests.asciiDataInColumns_2D, model, False)
coefficients = pyeq2.solverService().SolveUsingODR(model)
self.assertTrue(
numpy.allclose(coefficients,
coefficientsShouldBe,
rtol=1.0E-03,
atol=1.0E-300))
def test_SolveUsingDE_2D(self):
coefficientsShouldBe = numpy.array([-6.15515504031, 1.21618173729])
model = pyeq2.Models_2D.UserDefinedFunction.UserDefinedFunction(
'SSQABS', 'Default', 'm*X + b')
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(
DataForUnitTests.asciiDataInColumns_2D_small, model, False)
coefficients = pyeq2.solverService().SolveUsingDE(model)
self.assertTrue(
numpy.allclose(coefficients,
coefficientsShouldBe,
rtol=1.0E-06,
atol=1.0E-300))
def test_SolveUsingLinear_2D(self):
coefficientsShouldBe = numpy.array(
[-8.0191356407516956E+00, 1.5264472941853220E+00])
model = pyeq2.Models_2D.Polynomial.Linear('SSQABS')
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(
DataForUnitTests.asciiDataInColumns_2D, model, False)
coefficients = pyeq2.solverService().SolveUsingLinear(model)
self.assertTrue(
numpy.allclose(coefficients,
coefficientsShouldBe,
rtol=1.0E-10,
atol=1.0E-300))
def test_SolveUsingDE_3D(self):
coefficientsShouldBe = numpy.array(
[-0.206068766376, -0.644872592849, 1.13361007134])
model = pyeq2.Models_3D.UserDefinedFunction.UserDefinedFunction(
'SSQABS', 'Default', 'a + b*X + c*Y')
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(
DataForUnitTests.asciiDataInColumns_3D_small, model, False)
coefficients = pyeq2.solverService().SolveUsingDE(model)
self.assertTrue(
numpy.allclose(coefficients,
coefficientsShouldBe,
rtol=1.0E-06,
atol=1.0E-300))
def test_CalculateCoefficientAndFitStatisticsUsingSpline_2D(self):
model_df_e_ShouldBe = 8.0
model_df_r_ShouldBe = 2.0
model_r2_ShouldBe = 0.999870240966
model_rmse_ShouldBe = 0.0270425329959
model_r2adj_ShouldBe = 0.999837801207
model_Fstat_ShouldBe = 30822.3699783
model_Fpv_ShouldBe = 3.33066907388e-16
model_ll_ShouldBe = 24.1054640542
model_aic_ShouldBe = -3.83735710076
model_bic_ShouldBe = -3.72884020818
model_cov_beta_ShouldBe = None
model_sd_beta_ShouldBe = None
model_tstat_beta_ShouldBe = None
model_pstat_beta_ShouldBe = None
model_ci_ShouldBe = None
model = pyeq2.Models_2D.Spline.Spline(inSmoothingFactor = 1.0, inXOrder = 3)
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_2D, model, False)
pyeq2.solverService().SolveUsingSpline(model)
model.CalculateModelErrors(model.solvedCoefficients, model.dataCache.allDataCacheDictionary)
model.CalculateCoefficientAndFitStatistics()
self.assertTrue(numpy.allclose(model.df_e, model_df_e_ShouldBe, rtol=1.0E-06, atol=1.0E-300))
self.assertTrue(numpy.allclose(model.df_r, model_df_r_ShouldBe, rtol=1.0E-06, atol=1.0E-300))
self.assertTrue(numpy.allclose(model.r2, model_r2_ShouldBe, rtol=1.0E-06, atol=1.0E-300))
self.assertTrue(numpy.allclose(model.rmse, model_rmse_ShouldBe, rtol=1.0E-06, atol=1.0E-300))
self.assertTrue(numpy.allclose(model.r2adj, model_r2adj_ShouldBe, rtol=1.0E-06, atol=1.0E-300))
self.assertTrue(numpy.allclose(model.Fstat, model_Fstat_ShouldBe, rtol=1.0E-06, atol=1.0E-300))
self.assertTrue(numpy.allclose(model.Fpv, model_Fpv_ShouldBe, rtol=1.0E-06, atol=1.0E-300))
self.assertTrue(numpy.allclose(model.ll, model_ll_ShouldBe, rtol=1.0E-06, atol=1.0E-300))
self.assertTrue(numpy.allclose(model.aic, model_aic_ShouldBe, rtol=1.0E-06, atol=1.0E-300))
self.assertTrue(numpy.allclose(model.bic, model_bic_ShouldBe, rtol=1.0E-06, atol=1.0E-300))
self.assertEqual(model.cov_beta, None)
self.assertEqual(model.sd_beta, None)
self.assertEqual(model.tstat_beta, None)
self.assertEqual(model.pstat_beta, None)
self.assertEqual(model.ci, None)
def test_SolveUsingLinear_3D(self):
coefficientsShouldBe = numpy.array([
2.8658381589774945E-01, -9.0215775175410395E-01,
1.1548386445491325E+00
])
model = pyeq2.Models_3D.Polynomial.Linear('SSQABS')
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(
DataForUnitTests.asciiDataInColumns_3D, model, False)
coefficients = pyeq2.solverService().SolveUsingLinear(model)
self.assertTrue(
numpy.allclose(coefficients,
coefficientsShouldBe,
rtol=1.0E-10,
atol=1.0E-300))
def test_SolveUsingSpline_2D(self):
knotPointsShouldBe = numpy.array([5.357, 5.357, 5.357, 5.357, 9.861, 9.861, 9.861, 9.861])
coefficientsShouldBe = numpy.array([ 0.38297001, 1.95535226, 4.59605664, 7.16162379, 0.0, 0.0, 0.0, 0.0])
testEvaluationShouldBe = numpy.array([4.02361487093])
model = pyeq2.Models_2D.Spline.Spline(inSmoothingFactor = 1.0, inXOrder = 3)
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_2D, model, False)
fittedParameters = pyeq2.solverService().SolveUsingSpline(model)
# example of later using the saved spline knot points and coefficients
unFittedSpline = scipy.interpolate.fitpack2.UnivariateSpline(model.dataCache.allDataCacheDictionary['X'], model.dataCache.allDataCacheDictionary['DependentData'], s=model.smoothingFactor, k=model.xOrder)
unFittedSpline._eval_args = fittedParameters
testEvaluation = unFittedSpline(numpy.array([8.0]))
self.assertTrue(numpy.allclose(testEvaluation, testEvaluationShouldBe, rtol=1.0E-10, atol=1.0E-300))
self.assertTrue(numpy.equal(fittedParameters[0], knotPointsShouldBe).all())
self.assertTrue(numpy.allclose(fittedParameters[1], coefficientsShouldBe, rtol=1.0E-06, atol=1.0E-300))
def test_SolveUsingSpline_3D(self):
xKnotPointsShouldBe = numpy.array([0.607, 0.607, 0.607, 3.017, 3.017, 3.017])
yKnotPointsShouldBe = numpy.array([1.984, 1.984, 1.984, 3.153, 3.153, 3.153])
coefficientsShouldBe = numpy.array([2.33418963, 1.80079612, 5.07902936, 0.54445029, 1.04110843, 2.14180324, 0.26992805, 0.39148852, 0.8177307])
testEvaluationShouldBe = numpy.array([0.76020577997])
model = pyeq2.Models_3D.Spline.Spline(inSmoothingFactor = 1.0, inXOrder = 2, inYOrder = 2)
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_3D, model, False)
fittedParameters = pyeq2.solverService().SolveUsingSpline(model)
# example of later using the saved spline knot points and coefficients
unFittedSpline = scipy.interpolate.fitpack2.SmoothBivariateSpline(model.dataCache.allDataCacheDictionary['X'], model.dataCache.allDataCacheDictionary['Y'], model.dataCache.allDataCacheDictionary['DependentData'], s=model.smoothingFactor, kx=model.xOrder, ky=model.yOrder)
unFittedSpline.tck = fittedParameters
testEvaluation = unFittedSpline.ev(2.5, 2.5)
self.assertTrue(numpy.allclose(testEvaluation, testEvaluationShouldBe, rtol=1.0E-10, atol=1.0E-300))
self.assertTrue(numpy.equal(fittedParameters[0], xKnotPointsShouldBe).all())
self.assertTrue(numpy.equal(fittedParameters[1], yKnotPointsShouldBe).all())
self.assertTrue(numpy.allclose(fittedParameters[2], coefficientsShouldBe, rtol=1.0E-06, atol=1.0E-300))
def Solve(self, inNonLinearSolverAlgorithmName='Levenberg-Marquardt'):
solver = pyeq2.solverService()
# if any of these conditions exist, a linear solver cannot be used
if self.fixedCoefficients != [] or self.upperCoefficientBounds != [] or self.lowerCoefficientBounds != [] or len(self.dataCache.allDataCacheDictionary['Weights']):
self._canLinearSolverBeUsedForSSQABS = False
# selection of different solvers and algorithms.
if self.splineFlag:
return solver.SolveUsingSpline(self)
elif self.fittingTarget == 'SSQABS' and self.CanLinearSolverBeUsedForSSQABS() == True:
return solver.SolveUsingLinear(self)
elif self.fittingTarget == 'ODR':
if len(self.deEstimatedCoefficients) == 0:
self.deEstimatedCoefficients = solver.SolveUsingDE(self)
return solver.SolveUsingODR(self)
else:
if len(self.deEstimatedCoefficients) == 0:
self.deEstimatedCoefficients = solver.SolveUsingDE(self)
self.estimatedCoefficients = solver.SolveUsingSelectedAlgorithm(self, inAlgorithmName=inNonLinearSolverAlgorithmName)
return solver.SolveUsingSimplex(self)
def Solve(self, inNonLinearSolverAlgorithmName='Levenberg-Marquardt'):
solver = pyeq2.solverService()
# if any of these conditions exist, a linear solver cannot be used
if self.fixedCoefficients != [] or self.upperCoefficientBounds != [] or self.lowerCoefficientBounds != [] or len(self.dataCache.allDataCacheDictionary['Weights']):
self._canLinearSolverBeUsedForSSQABS = False
# selection of different solvers and algorithms.
if self.splineFlag:
return solver.SolveUsingSpline(self)
elif self.fittingTarget == 'SSQABS' and self.CanLinearSolverBeUsedForSSQABS(
) == True:
return solver.SolveUsingLinear(self)
elif self.fittingTarget == 'ODR':
if len(self.deEstimatedCoefficients) == 0:
self.deEstimatedCoefficients = solver.SolveUsingDE(self)
return solver.SolveUsingODR(self)
else:
if len(self.deEstimatedCoefficients) == 0:
self.deEstimatedCoefficients = solver.SolveUsingDE(self)
self.estimatedCoefficients = solver.SolveUsingSelectedAlgorithm(
self, inAlgorithmName=inNonLinearSolverAlgorithmName)
return solver.SolveUsingSimplex(self)
def Solve(self):
solver = pyeq2.solverService()
if self.splineFlag:
return solver.SolveUsingSpline(self)
if self.fixedCoefficients != []:
self._canLinearSolverBeUsedForSSQABS = False
if self.fittingTarget == 'SSQABS':
if self.CanLinearSolverBeUsedForSSQABS() == True:
return solver.SolveUsingLinear(self)
else:
self.estimatedCoefficients = solver.SolveUsingDE(self)
self.estimatedCoefficients = solver.SolveUsingLevenbergMarquardt(self)
return solver.SolveUsingSimplex(self)
if self.fittingTarget == 'ODR':
self.estimatedCoefficients = solver.SolveUsingDE(self)
return solver.SolveUsingODR(self)
# default
self.estimatedCoefficients = solver.SolveUsingDE(self)
return solver.SolveUsingSimplex(self)
6.58146965059
5.60990756287
9.53792438164
5.79102705637
5.33559365272
6.48809806813
7.66644773214
7.60246282819
7.23133792963
'''
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(
asciiTextData, simpleObject, False)
resultList = []
solver = pyeq2.solverService()
criteriaForUseInListSorting = 'AIC' # ['AIC', 'AICc_BA', 'nnlf'] from top of SolverService.SolveStatisticalDistribution()
for distribution in inspect.getmembers(
scipy.stats): # try to fit every distribution
if isinstance(distribution[1], scipy.stats.rv_continuous):
print("Fitting", distribution[0])
try:
result = solver.SolveStatisticalDistribution(
distribution[0], simpleObject.dataCache.
allDataCacheDictionary['IndependentData'][0],
criteriaForUseInListSorting)
except:
continue
if result:
resultList.append(result)
9.57246613925
6.58146965059
5.60990756287
9.53792438164
5.79102705637
5.33559365272
6.48809806813
7.66644773214
7.60246282819
7.23133792963
'''
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(asciiTextData, simpleObject, False)
resultList = []
solver = pyeq2.solverService()
criteriaForUseInListSorting = 'AIC' # ['AIC', 'AICc_BA', 'nnlf'] from top of SolverService.SolveStatisticalDistribution()
for distribution in inspect.getmembers(scipy.stats): # try to fit every distribution
if isinstance(distribution[1], scipy.stats.rv_continuous):
print("Fitting", distribution[0])
try:
result = solver.SolveStatisticalDistribution(distribution[0], simpleObject.dataCache.allDataCacheDictionary['IndependentData'][0], criteriaForUseInListSorting)
except:
continue
if result:
resultList.append(result)
print()
resultList.sort()
print(resultList[0])
def test_SolveUsingDE_2D(self):
coefficientsShouldBe = numpy.array([ -7.19386218627, 1.45067239435])
model = pyeq2.Models_2D.UserDefinedFunction.UserDefinedFunction('SSQABS', 'Default', 'm*X + b')
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_2D_small, model, False)
coefficients = pyeq2.solverService().SolveUsingDE(model)
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0E-05, atol=1.0E-300))
def test_SolveUsingLinear_2D(self):
coefficientsShouldBe = numpy.array([-8.0191356407516956e00, 1.5264472941853220e00])
model = pyeq2.Models_2D.Polynomial.Linear("SSQABS")
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_2D, model, False)
coefficients = pyeq2.solverService().SolveUsingLinear(model)
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0e-10, atol=1.0e-300))
def test_SolveUsingLinear_3D(self):
coefficientsShouldBe = numpy.array([2.8658381589774945e-01, -9.0215775175410395e-01, 1.1548386445491325e00])
model = pyeq2.Models_3D.Polynomial.Linear("SSQABS")
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_3D, model, False)
coefficients = pyeq2.solverService().SolveUsingLinear(model)
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0e-10, atol=1.0e-300))
def test_SolveUsingODR_2D(self):
coefficientsShouldBe = numpy.array([-8.04624, 1.53032])
model = pyeq2.Models_2D.Polynomial.Linear("ODR")
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(DataForUnitTests.asciiDataInColumns_2D, model, False)
coefficients = pyeq2.solverService().SolveUsingODR(model)
self.assertTrue(numpy.allclose(coefficients, coefficientsShouldBe, rtol=1.0e-03, atol=1.0e-300))
