سلام.من تازه دارم با پایتون کار میکنم و توی اجرای یه کد به مشکل خوردم.ممنون میشم کمکم کنید!

سلام.من خیلی تازه کارم در برنامه نویسی پایتون و یه مشکل تو اجرای یه کد دارم.میخواستم ببینم کسی اینجا هست به من کمک کنه!!!

اروری که توی اجراش میگیرم اینه :

Traceback (most recent call last):

File "C:\1.py", line 97, in <module>

U, S, Vt = computeSVD(urm, K)

File "C:\1.py", line 53, in computeSVD

U, s, Vt = sparsesvd(urm, K)

File "sparsesvd.pyx", line 10, in sparsesvd.sparsesvd (sparsesvd.c:2167)

TypeError: First argument must be a scipy.sparse.csc_matrix

ممنون میشم اگه کسی کمکم کنه...

کد هم اینه :

import numpy as np
from scipy.sparse import csr_matrix
import math as mt
import csv
from sparsesvd import sparsesvd
from scipy.sparse.linalg import * #used for matrix multiplication


#constants defining the dimensions of our User Rating Matrix (URM)
MAX_PID = 37143
MAX_UID = 15375


def readUrm():
    urm = np.zeros(shape=(MAX_UID,MAX_PID), dtype=np.float32)
	with open('C:\\train_v2.csv', 'rb') as trainFile:
		urmReader = csv.reader(trainFile, delimiter=',')
		for row in urmReader:
			urm[int(row[0]), int(row[1])] = float(row[2])

	return csr_matrix(urm, dtype=np.float32)
	
	
	
def readUsersTest():
	uTest = dict()
	with open("C:\\test_v2.csv", 'rb') as testFile:
		testReader = csv.reader(testFile, delimiter=',')
		for row in testReader:
			uTest[int(row[0])] = list()

	return uTest
	
	
	
	
def getMoviesSeen():
	moviesSeen = dict()
	with open("C:\\train_v2.csv", 'rb') as trainFile:
		urmReader = csv.reader(trainFile, delimiter=',')
		for row in urmReader:
			try:
				moviesSeen[int(row[0])].append(int(row[1]))
			except:
				moviesSeen[int(row[0])] = list()
				moviesSeen[int(row[0])].append(int(row[1]))

	return moviesSeen
	

def computeSVD(urm, K):
	U, s, Vt = sparsesvd(urm, K)

	dim = (len(s), len(s))
	S = np.zeros(dim, dtype=np.float32)
	for i in range(0, len(s)):
		S[i,i] = mt.sqrt(s[i])

	U = csr_matrix(np.transpose(U), dtype=np.float32)
	S = csr_matrix(S, dtype=np.float32)
	Vt = csr_matrix(Vt, dtype=np.float32)

	return U, S, Vt	
	





def computeEstimatedRatings(urm, U, S, Vt, uTest, moviesSeen, K, test):
	rightTerm = S*Vt 

	estimatedRatings = np.zeros(shape=(MAX_UID, MAX_PID), dtype=np.float16)
	for userTest in uTest:
		prod = U[userTest, :]*rightTerm

		#we convert the vector to dense format in order to get the indices of the movies with the best estimated ratings 
		estimatedRatings[userTest, :] = prod.todense()
		recom = (-estimatedRatings[userTest, :]).argsort()[:250]
		for r in recom:
			if r not in moviesSeen[userTest]:
				uTest[userTest].append(r)

				if len(uTest[userTest]) == 5:
					break

	return uTest
	
	
	

	

K = 90
urm = readUrm()
U, S, Vt = computeSVD(urm, K)
uTest = readUsersTest()
moviesSeen = getMoviesSeen()
uTest = computeEstimatedRatings(urm, U, S, Vt, uTest, moviesSeen, K, True)