Exemple #1
0
    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
Exemple #2
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    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
Exemple #3
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    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
Exemple #4
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    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
Exemple #5
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    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
Exemple #6
0
    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