Si votre fichier CSV est sans lignes d'en-tête (pas sûr si un fichier CSV en a besoin) et contient des chiffres, vous pouvez simplement appeler
x = importdata('<nameofyourcsv>.csv');
puis
c = cov(x);
ou même le faire en une seule étape:
c = cov(importdata('nameofcsv.csv'));
La fonction est équivalente à un clic sur le bouton que vous mentionnez. Toutefois, si votre fichier contient des lignes d'en-tête, vous devez spécifier son numéro et appeler cette fonction avec plusieurs paramètres:
x = importdata('nameofcsv.csv',',', nh); % nh is a number of header lines
x ne sera plus une matrice Matlab régulière, mais une structure avec des champs suivants: data, textdata, et colheaders. cov(x.data) calculera la covariance. Je ne sais pas pourquoi vous auriez besoin d'enregistrer quoi que ce soit dans un fichier mat, cependant.Si vous le souhaitez, vous devrez probablement attribuer une valeur d'un champ data à une variable régulière, puis enregistrer cette variable. Sinon, vous sauvegarderez la totalité de la structure x.
Par exemple, le fichier suivant, 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
peut être importé en appelant importdata:
>> 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
EDIT
J'ai téléchargé le classer et copier le passé ed cette commande de votre réponse:
c = importdata('nyse_data_matrix_no_tags.csv');
Ensuite, j'ai ouvert le fichier dans un éditeur de texte et copié le 1er et le 20 rangée de numéros dans le fichier et créé une matrice dans Matlab:
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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I matrice par rapport x avec le 1er et le 20 rangée de c et ils sont égaux:
isequal(x,c([1 20],:))
ans =
1
importdata semble fonctionner comme prévu.
S'il vous plaît voir mon commentaire pour plus d'informations sur le problème. Merci beaucoup pour votre temps et votre aide! – lel23