TypeError : 'map'유형의 객체에 len()이 없습니다. Python3

Question

Pempark를 사용하여 KMeans 알고리즘을 구현하려고하는데 이는 while 루프의 마지막 행에서 위의 오류를 발생시킵니다. 루프 외부에서 잘 작동하지만 루프를 생성 한 후에이 오류가 발생했습니다 어떻게 수정합니까? 당신이하지 않는 len객체 map의 (발전기 유형) 얻으려고 노력하고 있기 때문에

# Find K Means of Loudacre device status locations 
# 
# Input data: file(s) with device status data (delimited by '|') 
# including latitude (13th field) and longitude (14th field) of device locations 
# (lat,lon of 0,0 indicates unknown location) 
# NOTE: Copy to pyspark using %paste 

# for a point p and an array of points, return the index in the array of the point closest to p 
def closestPoint(p, points): 
    bestIndex = 0 
    closest = float("+inf") 
    # for each point in the array, calculate the distance to the test point, then return 
    # the index of the array point with the smallest distance 
    for i in range(len(points)): 
     dist = distanceSquared(p,points[i]) 
     if dist < closest: 
      closest = dist 
      bestIndex = i 
    return bestIndex 

# The squared distances between two points 
def distanceSquared(p1,p2): 
    return (p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2 

# The sum of two points 
def addPoints(p1,p2): 
    return [p1[0] + p2[0], p1[1] + p2[1]] 

# The files with device status data 
filename = "/loudacre/devicestatus_etl/*" 

# K is the number of means (center points of clusters) to find 
K = 5 

# ConvergeDist -- the threshold "distance" between iterations at which we decide we are done 
convergeDist=.1 

# Parse device status records into [latitude,longitude] 
rdd2=rdd1.map(lambda line:(float((line.split(",")[3])),float((line.split(",")[4])))) 
# Filter out records where lat/long is unavailable -- ie: 0/0 points 
# TODO 
filterd=rdd2.filter(lambda x:x!=(0,0)) 
# start with K randomly selected points from the dataset 
# TODO 
sample=filterd.takeSample(False,K,42) 
# loop until the total distance between one iteration's points and the next is less than the convergence distance specified 
tempDist =float("+inf") 
while tempDist > convergeDist: 
    # for each point, find the index of the closest kpoint. map to (index, (point,1)) 
    # TODO 
    indexed =filterd.map(lambda (x1,x2):(closestPoint((x1,x2),sample),((x1,x2),1))) 

    # For each key (k-point index), reduce by adding the coordinates and number of points 

    reduced=indexed.reduceByKey(lambda x,y: ((x[0][0]+y[0][0],x[0][1]+y[0][1]),x[1]+y[1])) 
    # For each key (k-point index), find a new point by calculating the average of each closest point 
    # TODO 
    newCenters=reduced.mapValues(lambda x1: [x1[0][0]/x1[1], x1[0][1]/x1[1]]).sortByKey() 
    # calculate the total of the distance between the current points and new points 
    newSample=newCenters.collect() #new centers as a list 
    samples=zip(newSample,sample) #sample=> old centers 
    samples1=sc.parallelize(samples) 
    totalDistance=samples1.map(lambda x:distanceSquared(x[0][1],x[1])) 
    # Copy the new points to the kPoints array for the next iteration 
    tempDist=totalDistance.sum() 
    sample=map(lambda x:x[1],samples) #new sample for next iteration as list 
sample 

Answer 1

이 오류를 얻고있다는 len을 지원합니다. 예를 들어 :

>>> x = [[1, 'a'], [2, 'b'], [3, 'c']] 

# `map` returns object of map type 
>>> map(lambda a: a[0], x) 
<map object at 0x101b75ba8> 

# on doing `len`, raises error 
>>> len(map(lambda a: a[0], x)) 
Traceback (most recent call last): 
    File "<stdin>", line 1, in <module> 
TypeError: object of type 'map' has no len() 

길이를 찾기 위해, 당신은 maplist에 (또는 tuple)를 캐스팅 입력해야합니다 그리고 당신은 그 위에 len를 호출 할 수 있습니다. 예를 들면 :

>>> len(list(map(lambda a: a[0], x))) 
3 

아니면로 (map를 사용하지 않고) 단순히 지능형리스트를 사용하여 목록을 만드는 더 나은입니다 :

이

>>> my_list = [a[0] for a in x] 

# since it is a `list`, you can take it's length 
>>> len(my_list) 
3