Logistic Neuron class

neuron_logistic.py

""" NeuronLogistic class to logistic neurons with logit input """
#     Field input receives a dictionaire where  key = number of the input unit,
# element = length 2 list with signal and synaptic weights respectively
import numpy as np

class NeuronLogistic:

    def __init__(self,presynaptics):

        if type(presynaptics) != dict:
            self.input = {};
            self.output = 0;                
        else:
            self.input = presynaptics;
            self.output = 0;
        self.isSynapse();

    def receiveNetInput(self,presynaptics):
        l = list()
        if type(presynaptics) != dict:
            self.input = {};            
        else:
            self.input = presynaptics;
        for k in self.input.iterkeys():
            if (type(k) != int) and (type(k) != str):
                l.append(k);
        for i in l:
            self.input.pop(i);
        self.isSynapse();

    def receiveInput(self,neuron,synapse):

        if (type(neuron) != int) and (type(neuron) != str):
            print('The first argument must be integer, neuron index, or string b for bias');
        else:
            self.input[neuron] = synapse;
        self.isSynapse();
    
    def processLogit(self):
        
        self.logit = 0;
        for v in self.input.itervalues():
            self.logit += v[0]*v[1];
       
    def processOutput(self):
        try:
            self.output = 1/(1+np.exp(-self.logit));
        except NameError:
            print('Neuron never calculated any logit, try processLogit before processOutput');

    def isSynapse(self):
        l = list();
        for k,v in self.input.iteritems():
            if type(v) != list:
                l.append(k);
            elif len(v) != 2:
                l.append(k);
            else:
                if type(v[0]) != float:
                    v[0] = 0.0;
                if type(v[1]) != float:
                    v[1] = 0.0;
        for i in l:
            self.input.pop(i);