def psignifitCore(data, options):
"""
This is the Core processing of psignifit, call the frontend psignifit!
function result=psignifitCore(data,options)
Data nx3 matrix with values [x, percCorrect, NTrials]
sigmoid should be a handle to a function, which accepts
X,parameters as inputs and gives back a value in [0,1]. ideally
parameters(1) should correspond to the threshold and parameters(2) to
the width (distance containing 95% of the function.
"""
d = len(options['borders'])
result = {'X1D': [], 'marginals': [], 'marginalsX': [], 'marginalsW': []}
'''Choose grid dynamically from data'''
if options['dynamicGrid']:
# get seed from linear regression with logit transform
Seed = getSeed(data,options)
# further optimize the logliklihood to obtain a good estimate of the MAP
if options['expType'] == 'YesNo':
calcSeed = lambda X: -_l.logLikelihood(data, options, X[0], X[1], X[2], X[3], X[4])
Seed = scipy.optimize.fmin(func=calcSeed, x0 = Seed)
elif options['expType'] == 'nAFC':
calcSeed = lambda X: -_l.logLikelihood(data, options, X[0], X[1], X[2], 1/options['expN'], X[3])
Seed = scipy.optimize.fmin(func=calcSeed, x0 = [Seed[0:2], Seed[4]])
Seed = [Seed[0:2], 1/options['expN'], Seed[3]] #ToDo check whether row or colum vector
result['X1D'] = gridSetting(data,options, Seed)
else: # for types which do not need a MAP estimate
if (options['gridSetType'] == 'priorlike' or options['gridSetType'] == 'STD'
or options['gridSetType'] == 'exp' or options['gridSetType'] == '4power'):
result['X1D'] = gridSetting(data,options)
else: # Use a linear grid
for idx in range(0,d):
# If there is an actual Interval
if options['borders'][idx, 0] < options['borders'][idx,1]:
result['X1D'].append(np.linspace(options['borders'][idx,0], options['borders'][idx,1],
num=options['stepN'][idx]))
# if parameter was fixed
else:
result['X1D'].append(np.array([options['borders'][idx,0]]))
'''Evaluate likelihood and form it into a posterior'''
(result['Posterior'], result['logPmax']) = _l.likelihood(data, options, result['X1D'])
result['weight'] = getWeights(result['X1D'])
integral = np.sum(np.array(result['Posterior'][:])*np.array(result['weight'][:]))
result['Posterior'] = result['Posterior']/integral
result['integral'] = integral
'''Compute marginal distributions'''
for idx in range(0,d):
m, mX, mW = marginalize(result, np.array([idx]))
result['marginals'].append(m)
result['marginalsX'].append(mX)
result['marginalsW'].append(mW)
result['marginals'] = np.squeeze(result['marginals'])
result['marginalsX'] = np.squeeze(result['marginalsX'])
result['marginalsW'] = np.squeeze(result['marginalsW'])
'''Find point estimate'''
if (options['estimateType'] in ['MAP','MLE']):
# get MLE estimate
#start at most likely grid point
index = np.where(result['Posterior'] == np.max(result['Posterior'].ravel()))
Fit = np.zeros([d,1])
for idx in range(0,d):
Fit[idx] = result['X1D'][idx][index[idx]]
if options['expType'] == 'YesNo':
fun = lambda X, f: -_l.logLikelihood(data, options, [X[0],X[1],X[2],X[3],X[4]])
x0 = _deepcopy(Fit)
a = None
elif options['expType'] == 'nAFC':
#def func(X,f):
# return -_l.logLikelihood(data,options, [X[0], X[1], X[2], f, X[3]])
#fun = func
fun = lambda X, f: -_l.logLikelihood(data,options, [X[0], X[1], X[2], f, X[3]])
x0 = _deepcopy(Fit[0:3]) # Fit[3] is excluded
x0 = np.append(x0,_deepcopy(Fit[4]))
a = np.array([1/options['expN']])
elif options['expType'] == 'equalAsymptote':
fun = lambda X, f: -_l.logLikelihood(data,options,[X[0], X[1], X[2], f, X[3]])
x0 = _deepcopy(Fit[0:3])
x0 = np.append(x0,_deepcopy(Fit[4]))
a = np.array([np.nan])
else:
raise ValueError('unknown expType')
if options['fastOptim']:
Fit = scipy.optimize.fmin(fun, x0, args = (a,), xtol=0, ftol = 0, maxiter = 100, maxfun=100)
warnings.warn('changed options for optimization')
else:
Fit = scipy.optimize.fmin(fun, x0, args = (a,), disp = False)
if options['expType'] == 'YesNo':
result['Fit'] = _deepcopy(Fit)
elif options['expType'] == 'nAFC':
fit = _deepcopy(Fit[0:3])
fit = np.append(fit, np.array([1/options['expN']]))
fit = np.append(fit, _deepcopy(Fit[3]))
result['Fit'] = fit
elif options['expType'] =='equalAsymptote':
fit = _deepcopy(Fit[0:3])
fit = np.append(fit, Fit[2])
fit = np.append(fit, Fit[3])
result['Fit'] = fit
else:
raise ValueError('unknown expType')
par_idx = np.where(np.isnan(options['fixedPars']) == False)
for idx in par_idx[0]:
result['Fit'][idx] = options['fixedPars'][idx]
elif options['estimateType'] == 'mean':
# get mean estimate
Fit = np.zeros([d,1])
for idx in range[0:d]:
Fit[idx] = np.sum(result['marginals'][idx]*result['marginalsW'][idx]*result['marginalsX'][idx])
result['Fit'] = _deepcopy(Fit)
Fit = np.empty(Fit.shape)
'''Include input into result'''
result['options'] = options # no copies here, because they are not changing
result['data'] = data
'''Compute confidence intervals'''
if ~options['fastOptim']:
result['conf_Intervals'] = getConfRegion(result)
return result
def psignifitCore(data, options):
"""
This is the Core processing of psignifit, call the frontend psignifit!
function result=psignifitCore(data,options)
Data nx3 matrix with values [x, percCorrect, NTrials]
sigmoid should be a handle to a function, which accepts
X,parameters as inputs and gives back a value in [0,1]. ideally
parameters(1) should correspond to the threshold and parameters(2) to
the width (distance containing 95% of the function.
"""
d = len(options['borders'])
result = {'X1D': [], 'marginals': [], 'marginalsX': [], 'marginalsW': []}
'''Choose grid dynamically from data'''
if options['dynamicGrid']:
# get seed from linear regression with logit transform
Seed = getSeed(data,options)
# further optimize the logliklihood to obtain a good estimate of the MAP
if options['expType'] == 'YesNo':
calcSeed = lambda X: -l.logLikelihood(data, options, X[0], X[1], X[2], X[3], X[4])
Seed = scipy.optimize.fmin(func=calcSeed, x0 = Seed)
elif options['expType'] == 'nAFC':
calcSeed = lambda X: -l.logLikelihood(data, options, X[0], X[1], X[2], 1/options['expN'], X[3])
Seed = scipy.optimize.fmin(func=calcSeed, x0 = [Seed[0:2], Seed[4]])
Seed = [Seed[0:2], 1/options['expN'], Seed[3]] #ToDo check whether row or colum vector
result['X1D'] = gridSetting(data,options, Seed)
else: # for types which do not need a MAP estimate
if (options['gridSetType'] == 'priorlike' or options['gridSetType'] == 'STD'
or options['gridSetType'] == 'exp' or options['gridSetType'] == '4power'):
result['X1D'] = gridSetting(data,options)
else: # Use a linear grid
for idx in range(0,d):
# If there is an actual Interval
if options['borders'][idx, 0] < options['borders'][idx,1]:
result['X1D'].append(np.linspace(options['borders'][idx,0], options['borders'][idx,1],
num=options['stepN'][idx]))
# if parameter was fixed
else:
result['X1D'].append(np.array([options['borders'][idx,0]]))
'''Evaluate likelihood and form it into a posterior'''
(result['Posterior'], result['logPmax']) = l.likelihood(data, options, result['X1D'])
result['weight'] = getWeights(result['X1D'])
integral = np.sum(np.array(result['Posterior'][:])*np.array(result['weight'][:]))
result['Posterior'] = result['Posterior']/integral
result['integral'] = integral
'''Compute marginal distributions'''
for idx in range(0,d):
m, mX, mW = marginalize(result, np.array([idx]))
result['marginals'].append(m)
result['marginalsX'].append(mX)
result['marginalsW'].append(mW)
result['marginals'] = np.squeeze(result['marginals'])
result['marginalsX'] = np.squeeze(result['marginalsX'])
result['marginalsW'] = np.squeeze(result['marginalsW'])
'''Find point estimate'''
if (options['estimateType'] in ['MAP','MLE']):
# get MLE estimate
#start at most likely grid point
index = np.where(result['Posterior'] == np.max(result['Posterior'].ravel()))
Fit = np.zeros([d,1])
for idx in range(0,d):
Fit[idx] = result['X1D'][idx][index[idx]]
if options['expType'] == 'YesNo':
fun = lambda X, f: -l.logLikelihood(data, options, [X[0],X[1],X[2],X[3],X[4]])
x0 = deepcopy(Fit)
a = None
elif options['expType'] == 'nAFC':
#def func(X,f):
# return -l.logLikelihood(data,options, [X[0], X[1], X[2], f, X[3]])
#fun = func
fun = lambda X, f: -l.logLikelihood(data,options, [X[0], X[1], X[2], f, X[3]])
x0 = deepcopy(Fit[0:3]) # Fit[3] is excluded
x0 = np.append(x0,deepcopy(Fit[4]))
a = np.array([1/options['expN']])
elif options['expType'] == 'equalAsymptote':
fun = lambda X, f: -l.logLikelihood(data,options,[X[0], X[1], X[2], f, X[3]])
x0 = deepcopy(Fit[0:3])
x0 = np.append(x0,deepcopy(Fit[4]))
a = np.array([np.nan])
else:
raise ValueError('unknown expType')
if options['fastOptim']:
Fit = scipy.optimize.fmin(fun, x0, args = (a,), xtol=0, ftol = 0, maxiter = 100, maxfun=100)
warnings.warn('changed options for optimization')
else:
Fit = scipy.optimize.fmin(fun, x0, args = (a,), disp = True)
if options['expType'] == 'YesNo':
result['Fit'] = deepcopy(Fit)
elif options['expType'] == 'nAFC':
fit = deepcopy(Fit[0:3])
fit = np.append(fit, np.array([1/options['expN']]))
fit = np.append(fit, deepcopy(Fit[3]))
result['Fit'] = fit
elif options['expType'] =='equalAsymptote':
fit = deepcopy(Fit[0:3])
fit = np.append(fit, Fit[2])
fit = np.append(fit, Fit[3])
result['Fit'] = fit
else:
raise ValueError('unknown expType')
par_idx = np.where(np.isnan(options['fixedPars']) == False)
for idx in par_idx:
result['Fit'][idx] = options['fixedPars'][idx]
elif options['estimateType'] == 'mean':
# get mean estimate
Fit = np.zeros([d,1])
for idx in range[0:d]:
Fit[idx] = np.sum(result['marginals'][idx]*result['marginalsW'][idx]*result['marginalsX'][idx])
result['Fit'] = deepcopy(Fit)
Fit = np.empty(Fit.shape)
'''Include input into result'''
result['options'] = options # no copies here, because they are not changing
result['data'] = data
'''Compute confidence intervals'''
if ~options['fastOptim']:
result['conf_Intervals'] = getConfRegion(result)
return result
options['dynamicGrid'] = 0
options['widthalpha'] = 0.05
options['threshPC'] = 0.5
options['CImethod'] = 'percentiles'
options['gridSetType'] = 'cumDist'
options['nblocks'] = 25
options['verbose'] = 0
#options['stimulusRange'] = 0
options['sigmoidHandle'] = getSigmoidHandle(options)
options['fastOptim'] = 0
options['mbStepN'] = np.array([30,40,10,1,20])
options['logspace'] = 0
temp_data= importer.loadmat('variables.mat', struct_as_record=True,matlab_compatible=True)
temp_options = importer.loadmat('options.mat', struct_as_record=False, squeeze_me=True)
var = np.array([ 0.00466446, 0.00473373, 0, 0.5 ,0])
- logLikelihood(data, options, var)
ar = np.random.random([15,10,5,3]);
alpha = np.ones([15,1,1,1])
alpha = np.tile(alpha, ar.shape[1:])
beta = np.ones([1,10,1,1])*0.5
beta[0,2,0,0] = 0.3
beta = np.tile(beta, (ar.shape[0],1)+ ar.shape[2:])
ar += alpha
