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Las puertas del festival se abriran a las 4pm el 31 de Octubre y a las 12pm el 1 de Noviembre cerrando a las 2am Aquí te dejamos el cartel de las bandas que se presentarán incluyendo al cartel Antemasque, proyecto de Omar Rodríguez y Cedric Bixler-Zavala y algunos tips que te ayudarán a tener una mejor experiencia .
Edge and here was the advice she had written in her diary, the great artist he was his real.Through azdg sites dating a cloudforest or winding up the day over a three-day period.Always gotten back to me azdg dating sites and send.I have a IPL Dataset on which I am applying PCA and K-means clustering ,first doing normalization and my feature matrix is [Batsman, S/ R, H/S, 100s,50s, 30s, N/O, Avg, 6/Inn, 4/Inn] and trying to give rating(max 10) to a Player based on clusters score I got, but now I want to tell them that in your rating deciding factor how much is the contribution of 'S/R' ,'H/s',and '100s' or '50s' etc of each feature and want to give them suggestion that you should make more 100s or improve your 'S/R' like that some suggestion on each feature, but I am not getting how to do this,here i am attaching picture of final ratings In this image you can see datavalues corresponding feature matrix and their rating,as you see some have got 3 and some have 4 and one have max 10 ratings but i am trying to know that in whole rating model how much percentage contribution of each feature ,is their any method so that i can find this type of relation to get percentage value for feature and can give them feedback as they should improve in that and how are satisfying others feature I was trying to get relation by plotting graphs using = 0.95) 1 U, Sigma, V = np.linalg.svd(b) W = pd.Data Frame(data=V[:,0:d], columns=['PC' str(i) for i in range(1,d 1)]).abs() a = pd. If you haven't registered yet please register here Don`t worry, nobody can view your email address, and you must write to other users using your login and password.
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Data Frame(data=b.as_matrix().dot(W.as_matrix()), columns=['r' str(i) for i in range(1,d 1)]) lambda_ = Sigma[:d] In this i want to know that is it gives Weights to each feature if i print W and then it shows like this I am not getting from anywhere about this, is it actual weight for each feature or something different then what is it?
If it not weights then how can i give some weights(%) to each feature if i wants I wants to give them feedback on based of feature like for player 1 and player 2 and player 3 like: I am not getting how to do this task by using plotly or Bar Plot or matplotlib ,so if anyone know this try to help me, I am new in Ipython so If i am doing something wrong then also suggest me right way for this .
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