我有一个CSV文件,其中包含一个数字矩阵,在两年内有超过两千家纽约证券交易所上市公司的价值。
看起来它应该非常简单 - 我想获得一个由CSV矩阵组成的协方差矩阵。
据我所知,我只需要:
Import Data
按钮将数据导入为数字矩阵(只有数据无标题等)。save as
,例如NYSE.mat
。NYSE.mat
); 这应该访问矩阵并从我的数据中返回一个大的协方差矩阵。当我手动输入例如:
的示例矩阵时,cov()
函数有效
[5 0 3 7; 1 -5 7 3; 4 9 8 10];
但是出于某种原因,每当我尝试拨打cov(NYSE.mat
)时;只返回一个数字,而不是协方差矩阵。
谁能告诉我哪里出错了?我一直试图解决这个问题一段时间,我觉得答案应该很简单。
我在MATLAB R2016a上运行。
答案 0 :(得分:0)
不确定是否需要在步骤2中手动保存工作区名称。作为导入过程的一部分,单击导入按钮后,应将变量加载到具有文件名称的工作区(可能是纽约证券交易所)
尝试Load('NYSE.mat')
并查看工作区中显示的内容。
一旦找出变量的名称,用该函数调用该函数。
答案 1 :(得分:0)
如果您的CSV文件没有任何标题行(不确定是否有任何CSV文件需要)并且只包含数字,则只需调用
x = importdata('<nameofyourcsv>.csv');
然后
c = cov(x);
甚至可以一步到位:
c = cov(importdata('nameofcsv.csv'));
此功能相当于点击你提到的按钮。但是,如果您的文件包含标题行,则必须指定其编号并使用更多参数调用此函数:
x = importdata('nameofcsv.csv',',', nh); % nh is a number of header lines
x
将不再是常规的MATLAB矩阵,而是具有以下字段的结构:data
,textdata
和colheaders
。 cov(x.data)
将计算协方差。不知道为什么你需要在mat文件中保存任何东西。如果您愿意,您可能需要将data
字段中的值分配给常规变量,然后保存此变量。否则,您将保存整个x
结构。
例如,以下文件test.csv:
95.01,76.21,61.54,40.57,5.79,20.28,1.53
23.11,45.65,79.19,93.55,35.29,19.87,74.68
60.68,1.85,92.18,91.69,81.32,60.38,44.51
48.60,82.14,73.82,41.03,0.99,27.22,93.18
89.13,44.47,17.63,89.36,13.89,19.88,46.60
可以通过调用importdata
导入:
>> x = importdata('test.csv')
x =
95.0100 76.2100 61.5400 40.5700 5.7900 20.2800 1.5300
23.1100 45.6500 79.1900 93.5500 35.2900 19.8700 74.6800
60.6800 1.8500 92.1800 91.6900 81.3200 60.3800 44.5100
48.6000 82.1400 73.8200 41.0300 0.9900 27.2200 93.1800
89.1300 44.4700 17.6300 89.3600 13.8900 19.8800 46.6000
修改
我从你的回答中下载了文件并复制粘贴了这个命令:
c = importdata('nyse_data_matrix_no_tags.csv');
然后,我在文本编辑器中打开文件,并在文件中复制第1行和第20行数字,并在MATLAB中创建一个矩阵:
x = [46.09,24.69,156.78,5.95,21.14,76.17,55.51,7.04,38.87,19.58,47.57,7.73,119.44,1.61,44.55,24.9,50.89,4.87,26.25,15.95,15.96,39.14,121.27,15.7,25.91,25.8,69.7,16.32,12.86,7.89,247.4,27.11,41.6,5.14,47.77,40.98,13.78,26.058,14.56,41.05,24.27,47.13,40.92,27,85.04,15.06,5.05,29.14,7.51,67.5,67.79,42.68,124.86,24.28,27.82,18.08,100.67,109.07,12.59,50.34,18.64,4.75,6.06,16.63,109.86,14.1,54.48,13.9,59.98,16.24,45.53,4.06,67.99,18.22,4.2,117.23,158.75,5.7,2.46,76.39,77.79,6.17,16.95,5.27,12.74,14.7,19.14,14.4,42.77,26.4,14.17,76.57,12.4,42.07,77.47,41.27,60.53,16.97,65.71,56.13,258.4,28.96,760.07,60.46,34.77,133.1,14.19,29.41,11.78,154.55,44.75,15.18,17.52,24.86,36.18,14.1,30.1,47.65,22.92,47.37,61.84,226.52,10.2,65.14,7.88,16.34,170.23,9.6,34.42,15.0999,20.34,58.7,16.6,14.72,14.32,20.89,14.32,13.2,6.05,422.96,21.43,47.55,13.28,27.98,8.16,45.15,15.1665,41.65,17.45,25.73,63.03,16.07,17.56,11.54,6.54,33.18,15.01,357.96,74.88,15.24,27.24,69.08,36.29,76.55,65.72,50.93,72.73,16.13,23.81,52.14,16.05,12.19,71.77,14.97,11.82,33.04,7.86,15.26,72.46,15.42,24.49,15.75,63.78,32.43,36.57,16.24,7.08,26.08,15.74,14.78,16.22,16.85,23.32,31.14,12.72,6.7,26.65,24,12.95,129.92,20.07,11.28,34.66,12.86,17.5,26.5,0.7038,1.22,128.98,23.33,19.2,15.64,8.34,45.01,38.14,50.31,13.11,17.4,47.15,79.29,8.24,28.92,24.97,1.92,77.16,126.5,8.97,3.97,12.85,30.75,37.82,2.36,10.37,52.28,1.56,53,47.33,41.32,10.65,15.24,39.5,94.42,12,54.25,48.03,3.72,6.34,18.5,23.47,26.74,8.74,6.7,7.08,70.92,27.3,65.43,18.776,7.82,16.8,12.0625,124.24,29.79,22.88,29.4,66.44,28.88,1.505,0.8642,10.37,47.14,99.83,133.87,46.41,4.88,57.63,14.45,11.2,34.99,11.06,129.25,7.71,35.2,2.29,2.53,1.65,13.74,15.6,11.84,59.79,33.85,62.48,72.34,25.15,131.2,61.13,2.17,5.1,53.35,26.9,43.15,10.8,63.61,130.91,81.42,45.29,17.58,415.79,118.64,73.88,9.85,79.22,43.39,4.86,32.18,68.36,60.21,34.15,19.47,23.39,29.96,39.58,14.66,5.98,70.87,25.9,38.64,90.45,165.2,46.57,83.18,16.48,134.15,54.68,62.51,12.25,24.33,17.69,60.73,11.51,15.6,85.99,80.81,59.9,17.48,30.73,104.34,0.94,86.03,82.73,14.41,34.05,17.2,11.98,13,52.5,39.2,19.58,101.59,26.38,44.13,18.72,23.3,32.05,27,26.12,13.13,18.58,6.07,73.74,3.53,42.4,12.21,15.72,16.71,26.27,26.69,4.52,35.99,26.1,17.31,45.61,67.9,11.15,13.08,1.1,9.7001,17.51,59.8,26.27,86.95,18.95,26.75,34.33,66.72,107.98,9.7,18.27,24.16,56.8,46.56,91.69,21.43,78.75,3.37,13.71,31.73,100.39,5.65,6.98,85.21,97.84,13.5,26.19,42.43,26.73,10.11,47.95,102.84,66.95,28.35,14.07,5.25,23.68,127.43,11.41,10.43,4.19,25.48,19.78,72.08,53.59,17.41,36.57,92.37,127.48,23.98,16.46,5.28,24.76,9.1,68.33,64.45,8.15,18.54,8.85,119.72,3.62,20.49,2.57,94.05,16.99,25.93,25.12,25.71,9.68,81.26,16.7,77.21,37.45,80.43,6.95,24.95,87.1,20.74,31.82,25.44,25.48,25.5515,46.96,19.11,43.49,10,8.68,16.99,120.79,3.95,1.91,76.54,7.8,33.71,14.59,13.5,15.99,42.6,38.86,46.76,8.1,23.14,23.04,17.39,15.4399,13.62,14,125.94,13.92,22.98,48.68,4.62,68.17,1.14,9.82,29.75,73.66,6.51,92.28,25.53,25.6,25.63,7.59,72.9,25.23,10.41,27.87,10.81,48.76,11.38,3.76,71.78,13.21,53.59,25.55,42.43,68.21,66.25,37.2,6.1,6.24,84.58,13.17,13.2,22.92,82.95,28.9,6.07,74.57,29.35,74.89,64.46,77.91,30.21,11.09,6.34,21.69,56.88,15.25,41.44,1.77,67.42,209.26,11.24,15.8,13.53,15.09,32.8,9.78,12.99,62.77,21.8,39.11,78.68,14.75,10.67,20.6,10.57,36.39,7.24,6.1,13.61,14.58,51.04,20.36,102.48,35.14,12.31,8.98,3.7,81.22,89.67,27.32,13.26,1.54,37.84,11.29,8.98,24.08,4.43,8.54,15.51,36.26,9.33,6.56,43.38,13.04,10.9,54.68,160,164.11,34.25,15.87,14.65,18.72,10.57,13.25,21.08,62.55,6.08,22.72,17.25,14.1,11.91,16.5,17.36,4.93,31.46,75.14,32.84,55.65,21.39,18.76,53.36,52.27,150,11.51,50.35,4.77,16.2,13.83,43.24,94.4,37.89,13.18,35.9,70.65,14.16,11.8273,12.67,22.93,11.11,13.55,9.55,14.24,26.44,13.49,69.92,9.88,157.12,14.93,6.53,13.5,6.83,28.27,13.07,48.33,59,9.67,2.04,28.2,5.82,31.87,65.9,33.14,36.14,30.08,8.49,64.92,4.76,141.98,8.63,10.11,17.27,19.65,30.24,26.53,40.67,26.87,26.25,39.18,1.54,33.93,16.59,7.33,15.36,13.91,1.1,17.12,4.47,15.67,18.68,1.79,15.54,81.13,21.34,27.59,7.46,10.77,503.01,19.74,18.46,15.26,47.57,30.24,6.55,65.34,4.13,13.2,21.55,21.03,29.36,27.27,47.72,10,26.17,7.45,7.62,3.48,18.15,8.2,20.66,97.23,59.46,13.37,7.8,76.6,19.31,9.21,24.18,77.53,0.9,10.8,153.17,23.32,1.18,25.83,42.06,1.48,11.49,24.78,18.24,21.24,6.59,44.54,33.28,64.61,227.17,30,48.14,29.72,45.5,79.45,26.83,4.96,81.03,30.76,72.15,34.48,129.3,65.48,33.48,67.43,34.31,15.05,60.38,32.59,26.84,1.7,32.48,2.31,113.96,1.13,7.3,31.13,12.27,44.48,163.75,4.55,4.93,49.64,7,0.42,4.66,62.44,41.56,21.6,8.18,26.89,33.78,3.79,47.23,27.86,0.5199,45.15,117.21,9.51,74.52,1.8,67.24,22.28,22.735,28.48,13.84,19.61,26.62,25.78,18.95,33.53,21.54,51.22,13.79,34.9,81.22,6.86,15.59,96.42,18.49,31.21,24.57,21.23,22.93,16.32,10.63,11.18,188.35,16.08,18.39,18.17,43.96,62.54,8.82,1.87,33.38,14.9,10.47,16.69,3.2,8.89,3.95,50.02,153.42,7.65,35.53,0.54,18.36,5.24,262.52,4.07,74.98,12.53,13.66,86.82,129.8,24.78,9.8,7.03,21.68,8.1,17.1,7.5,38.4,118.45,6.47,26.92,17.2,7.6,35.32,1.72,25.83,9.11,26.27,13,12.18,12.29,44.75,26.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我将矩阵x
与c
的第1行和第20行进行比较,它们是相等的:
isequal(x,c([1 20],:))
ans =
1
importdata
似乎按预期工作。