我有一个CSV文件,其中包含以下列:
id,index,value,lenght
1,2 9 5,2 9 5,10
2,3 5 8,3 5 8,10
3,1,7,1
这是关于稀疏向量、向量的id和长度、向量的值以及向量的索引的信息。你知道吗
我想解析以下信息:id1,ide2,ide3,index1,index2,index3,value1,value2,value3,lenght1,lenght2,lenght3。你知道吗
我想用这些信息来执行一些操作,比如稀疏向量的加法和乘法。你知道吗
到目前为止,我的代码如下所示:
from __future__ import division
from sympy import *
import numpy as np
import csv
readVektor = csv.DictReader(open("data.csv"))
for column in readVektor:
print (column)
for column in readVektor:
id = int(column["id"])
index = int(column["index"])
value = int(column["value"])
length = int(column["lenght"])
#with open('data.csv', newline='') as readVektor: # Einlesen der Daten für Vektor 1
# data = csv.reader(readVektor, delimiter=';', quotechar='|')
# for row in data:
# print('; '.join(row))
#pprint(data)
class sparse(object): # Klasse, deren Objekte sparse Vektoren sind
def __init__(self, index, value, lenght, skalar): # Konstruktor
self.Index = index
self.Value = value
self.Laenge = lenght
self.Skalar = skalar
def maxNorm(self, value): # Berechnung der Maxmimums-Norm
maxNorm = max(value)
max_idx = value.index(maxNorm)
#return max_idx, maxNorm
print ("maxNorm: ", maxNorm)
def sMult(self, skalar, value): # Skalarmultiplikation
sMult = skalar * value
#return sMult
print ("sMult: ", sMult)
def Summe(self, index1, index2, value1, value2): # Berechnung der Summe
#for i, j in range(len(index1, index2)):
# if index1(i) < index2(i):
# self.summe(i) = value1(i)
# if index1(i) > index2(i):
# self.summe(i) = value2(i)
# if index1(i) == index2(i):
# self.summe(i) = value1(i) + value2(i)
# return summe
summe = value1 + value2
#return summe
print ("summe: ", summe)
def Differenz(self, index1, index2, value1, value2):
differenz = value1 - value2
#return differenz
print ("differenz: ", differenz)
def iProd(self, value1, value2):
iProd = np.dot (value1, value2)
#return iProd
print ("iProd: ", iProd)
#def Differenz(self, index1, index2, value1, value2): # Berechnung der Differenz
# for i in range(len(index1, index2))
# if index1(i) < index2(i)
# self.differenz(i) = value1(i)
# if index1(i) > index2(i)
# self.differenz(i) = value2(i)
# if index1(i) == index2(i)
# self.differenz(i) = value1(i) - value1(i)
#self.differenz = value1 - value2
#def Skalarprodukt(self, value1, value2): #Skalarprodukt
# for i in range(len(index1, index2))
# if index1(i) < index2(i)
# self.skalarprodukt(i) = 0
# if index1(i) > index2(i)
# self.skalarprodukt(i) = 0
# if index1(i) == index2(i)
# self.skalarprodukt(i) = value1(i) * value1(i)
#self.skalarprodukt = np.dot(value1, value2)
#print ("maxNorm: ", maxNorm)
#print ("summe: ", summe)
#print ("differenz: ", differenz)
#print ("sMult: ", sMult)
#print ("iProd: ", iProd)
sparsevector1 = sparse([2,9,5],[2,9,5],10,7)
sparsevector2 = sparse([3,5,8],[3,5,8],10,7)
sparsevector1.maxNorm([2,9,5])
sparsevector2.maxNorm([3,5,8])
sparsevector1.sMult(7,[2,9,5])
sparsevector2.sMult(7,[3,5,8])
sparsevector1.Summe([2,9,5],[3,5,8],[2,9,5],[3,5,8])
sparsevector2.Summe([2,9,5],[3,5,8],[2,9,5],[3,5,8])
#sparsevector1.Differenz([2,9,5],[3,5,8],[2,9,5],[3,5,8])
#sparsevector2.Differenz([2,9,5],[3,5,8],[2,9,5],[3,5,8])
sparsevector1.iProd([2,9,5],[3,5,8])
sparsevector2.iProd([2,9,5],[3,5,8])
如何解析必要的信息并将其传递给函数?你知道吗
您的稀疏类似乎将数组作为输入…请尝试以下操作:
那么索引和值都将是整数列表。你知道吗
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