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manager.py
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manager.py
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import stimgen
import weights
import numpy as np
from model import ConceptModel
class ExperimentManager(object):
"""
Runs the model for a specific number of times while logging results and
computing performance accuracy on a particular experimental task. For
example, the Experiment Manager will run the Posner experiment for 32
simulated participants with stimuli generated at a particular distortion
level.
Parameters:
-----------
dimensions : int
The dimensionality of the representations used in the experiment.
seeds : list
A list of random number seeds corresponding to a set of simuluated
participants in the experiment. The seeds are used to generate the
model and fix the selection of neuron parameters, tuning curves, etc.
dvals : list
The standard deviation of the gaussian distribution used to distort
the stimuli in various experimental conditions.
"""
def __init__(self, dimensions, seeds, dvals, raster=False):
self.dim = dimensions
self.dvals = dvals
self.seeds = seeds
self.raster = raster
def posner(self):
tally = np.zeros(4)
total = np.zeros((len(self.seeds), 4))
counts = np.array([6,3,6,6], float)
results = np.zeros((len(self.dvals), 4))
for dval in self.dvals:
for seed in self.seeds:
np.random.seed(seed)
stimuli = stimgen.Posner(self.dim, dval)
for stimulus in stimuli.test_vectors:
print 'Testing ', stimulus
model = ConceptModel.posner(self.dim,
stimulus,
stimuli,
seed,
raster=self.raster)
model.run()
if model.result == True:
label = self.posner_labels(stimulus)
tally[label] += 1
print 'Status: ', np.where(self.seeds == seed)[0][0], \
stimulus, tally, seed
error = 1 - np.divide(tally, counts)
total[np.where(self.seeds == seed)[0][0], :] += error
tally = np.zeros(4)
average = np.divide(np.sum(total, axis=0), float(len(self.seeds)))
results[np.where(self.dvals == dval)[0][0], :] = average
total = np.zeros((len(self.seeds), 4))
np.save('results/Posner'+str(self.dvals[0]), results)
print results
return results
def brooks(self):
total = np.zeros((len(self.seeds), 3))
counts = np.array([8, 4, 4], float)
tally = np.zeros(3)
results = np.zeros((len(self.dvals), 3))
for dval in self.dvals:
for seed in self.seeds:
np.random.seed(seed)
stimuli = stimgen.Brooks(self.dim, dval)
if np.where(self.seeds == seed)[0][0] < 4:
stimuli.rule1()
elif np.where(self.seeds == seed)[0][0] < 8:
stimuli.rule2()
elif np.where(self.seeds == seed)[0][0] < 12:
stimuli.rule3()
else:
stimuli.rule4()
for stimulus in stimuli.test_vectors:
print 'Testing ', stimulus
model = ConceptModel.brooks(self.dim,
stimulus,
stimuli,
seed,
raster=self.raster)
model.run()
if model.result == True:
label = self.brooks_labels(stimulus, stimuli)
tally[label] += 1
print 'Status: ', np.where(self.seeds == seed)[0][0], \
stimulus, tally, seed
error = 1 - np.divide(tally, counts)
total[np.where(self.seeds == seed)[0][0], :] = error
tally = np.zeros(3)
average = np.divide(np.sum(total, axis=0), float(len(self.seeds)))
results[np.where(self.dvals == dval)[0][0], :] = average
total = np.zeros((len(self.seeds), 3))
np.save('results/Brooks'+str(self.dvals[0]), results)
print results
return results
def murphy(self):
tally = np.zeros(4)
counts = np.array([8, 8, 8, 8], float)
results = np.zeros((len(self.seeds), 4))
for seed in self.seeds:
stimuli = stimgen.Murphy(self.dim, self.dvals)
weight = weights.murphy(seed)
for stimulus in stimuli.stimuli:
print 'Testing ', stimulus
c = self.count(stimulus)
w = weight[0][c,:]
p = weight[1][c,:]
print 'Weights', w
print 'Products', p
if 'Prototype' in stimulus:
s = [1,1,1,1]
model = ConceptModel.murphy(self.dim,
s, stimuli,
w, c, seed,
raster=self.raster)
model.run()
elif 'ConsistentA' in stimulus:
s = [1,0,1,1]
model = ConceptModel.murphy(self.dim,
s, stimuli,
w, c, seed,
raster=self.raster)
model.run()
elif 'ConsistentB' in stimulus:
s = [0,1,1,1]
model = ConceptModel.murphy(self.dim,
s, stimuli,
w, c, seed,
raster=self.raster)
model.run()
else:
s = [0,0,0,1]
model = ConceptModel.murphy(self.dim,
s, stimuli,
w, c, seed,
raster=self.raster)
model.run()
if model.result == True:
label = self.murphy_labels(stimulus)
tally[label] += 1
print 'Status: ', np.where(self.seeds == seed)[0][0], \
stimulus, tally, seed
error = np.divide(tally, counts)
results[np.where(self.seeds == seed)[0][0], :] = error
tally = np.zeros(4)
average = np.divide(np.sum(results, axis=0), float(len(self.seeds)))
np.save('results/Murphy', average)
print average
return average
def posner_labels(self, stimulus):
if 'T' in stimulus:
return 0
elif 'L' in stimulus:
return 2
elif 'H' in stimulus:
return 3
else:
return 1
def brooks_labels(self, stimulus, stimuli):
if stimuli.labelled_stimuli[stimulus][1] == 'Training Item':
return 0
elif stimuli.labelled_stimuli[stimulus][1] == 'Good Transfer':
return 1
else:
return 2
def murphy_labels(self, stimulus):
if 'Prototype' in stimulus:
return 0
elif 'ConsistentA' in stimulus:
return 1
elif 'ConsistentB' in stimulus:
return 2
else:
return 3
def count(self, stimulus):
if '0' in stimulus:
count = 0
elif '1' in stimulus:
count = 1
elif '2' in stimulus:
count = 2
elif '3' in stimulus:
count = 3
elif '4' in stimulus:
count = 4
elif '5' in stimulus:
count = 5
elif '6' in stimulus:
count = 6
else:
count = 7
return count