RuntimeError: matplotlib does not support generators as input

Question

I'm using fuzzy art algorithm to be applying on my data and everything works fine in case of results but when it come to plot the result the interpreter says: RuntimeError: matplotlib does not support generators as input.

By the way, i'm using python as a programming language and matplotlib to plot the results.

import numpy as np
from sys import argv
import matplotlib.pyplot as plt
from random import shuffle

def scaledList(list):
    min = None
    max = None
    for i in range(len(list)):
        if min is None or min > list[i]:
            min = list[i]
        if max is None or max < list[i]:
            max = list[i]
    for i in range(len(list)):
        list[i] = (float)(list[i] - min)/(max - min)
    #print "min = " + str(min) + ", max = " + str(max)
    return list

def complementCodedConversion(list1, list2):
    list = []
    for i in range(len(list1)):
        complementVector = (list1[i], list2[i], 1-list1[i], 1-list2[i])
        list.append(complementVector)
    return list

def fuzzyAnd(tuple1, tuple2):
    ls = []
    for i in range(len(tuple1)):
        minimum = min(tuple1[i], tuple2[i])
        ls.append(minimum)
    #print(ls)
    return ls


def printData(list):
    for i in range(len(list)):
        print (list[i])


def myPlot(dataList, boxList):
    x = map(lambda item:item[0], dataList)
    y = map(lambda item:item[1], dataList)

    plt.scatter(x, y)
    for i in range(len(boxList)):
        (a,b,c,d) = boxList[i]
        plt.plot([a,c,c,a,a], [b, b, d, d, b])

    plt.show()



training = np.loadtxt(fname="C:\\Users\\Ali\Desktop\\fuzzy-art-neural-network-master\\TrainingData.txt")
testing = np.loadtxt(fname="C:\\Users\\Ali\Desktop\\fuzzy-art-neural-network-master\\TestingData.txt")

trainingData = complementCodedConversion(scaledList(training[:, 2].tolist()), scaledList(training[:, 3].tolist()))
testingData = complementCodedConversion(scaledList(testing[:, 2].tolist()), scaledList(testing[:, 3].tolist()))

#printData(trainingData)

#parameters setting
#learning rate
beta = 1
#vigilance
rho = 0.8
alpha = 0.000001
categoryList = []
#shuffle(trainingData)

while True:
    #shuffle(trainingData)
    len1 = len(categoryList)
    for i in range(len(categoryList)):
        tjList = []
        for j in range(len(categoryList)):
            summation1 = fuzzyAnd(trainingData[i], categoryList[j])
            #summation2 = summation1[i] / (alpha+sum(categoryList[j]))
            ss1 = sum(categoryList[j])
            ss2 = alpha + ss1
            summation2 = summation1[i] / ss2
            tjList.append((summation2))

        tjList = sorted(tjList, key=lambda item:item[1])
        noMatchFlag = True
        while len(tjList) != 0 :
            (index, value) = tjList.pop(0)
            if sum(fuzzyAnd(trainingData[i], categoryList[index]))/sum(trainingData[i]) >= rho :
                categoryList[index] = map(lambda x, y: x*beta + y*(1-beta),
                                          fuzzyAnd(trainingData[i], categoryList[index]), categoryList[index])
                noMatchFlag = False
                break
        if noMatchFlag:
            categoryList.append(trainingData[i])

    novellist = []
    for i in range(len(testingData)):
        tjList = []
        for j in range(len(categoryList)):
            tjList.append((j, sum(fuzzyAnd(testingData[i], categoryList[j]))/(alpha+sum(categoryList[j]))))
        tjList = sorted(tjList, key=lambda item:item[1])
        noMatchFlag = True
        while len(tjList) != 0 :
            (index, value) = tjList.pop(0)
            if sum(fuzzyAnd(testingData[i], categoryList[index]))/sum(testingData[i]) >= rho :
                noMatchFlag = False
                break
        if noMatchFlag:
            novellist.append(i)

    print ("***************")
    printData(categoryList)
    print ("***************")
    print ("Novel list:")
    print (novellist)
    myPlot(trainingData, categoryList)
    #print "novelist: ", len(novellist)
    len2 = len(categoryList)
    if len2 == len1 :
        break

#print "number of centers: ", len2

The result of the interpreter in the image below:

How can i solve this problem to plot my results. Thanks in advance

Answer 1

the function map defines a generator instead of returning an object such as a list or a tuple.

In the function myPlot, you use map to create your scatter plot data, which is what matplotlib errors on. These calls should first be converted to a list or tuple, e.g., list(map(...)).

Answer 2

In that example there is a line which produces a list in python 2.7 but in python 3 it is just a generator. The "map" is strange anyways, so I'd recommend to replace that line.