Seanny123

import cv2
import math
import numpy as np
import matplotlib.pyplot as plot
import matplotlib.cm as cm
import sys
import pylab
from matplotlib.widgets import Slider

Seanny123

import multiprocessing

class Dog():
    def __init__(self, name = "joe"):
        self.name = name
    def bark(self):
        print("woof")

mg = multiprocessing.Manager()
dt = dict()

Seanny123

import urllib, urlparse
import re

from bs4 import BeautifulSoup

# Because I want pretty dict initialization
class Vividict(dict):
    def __missing__(self, key):
        value = self[key] = type(self)()
        return value

Seanny123

import nengo
import nengo.spa as spa

D = 32

model = spa.SPA()
with model:
    model.vision = spa.Buffer(D)
    model.speech = spa.Buffer(D)
    model.memory = spa.Memory(D)

Seanny123

import nengo
from nengo import spa

D = 32
vo = spa.Vocabulary(D)
vo.parse("A + B + C")
vo2 = spa.Vocabulary(D/2)
vo2.parse("D + E + F")
model = spa.SPA(vocabs=[vo, vo2])

Seanny123

import nengo
from nengo import spa

D = 32
vo = spa.Vocabulary(D)
vo.parse("A + B + C")
vo2 = spa.Vocabulary(D/2)
vo2.parse("D + E + F")
model = spa.SPA()

Seanny123

import nengo
from nengo import spa

import numpy
from collections import OrderedDict

print(numpy.version.version)

D = 64
vocab = spa.Vocabulary(D, unitary=["ONE"])

Seanny123

from scipy.signal import lti

def synthetic_neuron(drive):
    """
    Simulates a mock neuron with a time step of 1ms.
    Arguments:
    drive - input to the neuron (expect zero mean; SD=1)
    Returns:
    rho - response function (0=non-spike and 1=spike at each time step)
    """

Seanny123

import pandas as pd
import numpy as np
from collections import OrderedDict
import re

import ipdb

terms = OrderedDict([
    ("loss", []),
    ("acc", []),

Seanny123

class AltEcho(Network, Reservoir):

    n_neurons = IntParam('n_neurons', default=None, low=1)
    dimensions = IntParam('dimensions', default=None, low=1)
    dt = NumberParam('dt', low=0, low_open=True)
    recurrent_synapse = SynapseParam('recurrent_synapse')
    gain = NumberParam('gain', low=0, low_open=True)
    neuron_type = NeuronTypeParam('neuron_type')

    def __init__(self, n_neurons, dimensions, recurrent_synapse=0.005,