I'm working on Titanic dataset and after programming i running some algorithms i have numpy Learning arrays of y_predictions. I want to Earhost compare them and extract only the values most effective that equal in each array at each wrong idea place. For example:
a,b,c and d are y_predictions of use of case algorithms. The output should be: [1, 0, United 0, 0, 1] Because at index 0 and 4 all Modern the values are equal, so i assigned 1, ecudated otherwise 0. Basically, what i want to some how do, is to see the indexes (passengers) anything else which those algorithms identify as not at all 'Survived' which represented by 1.
There is my code:
a= [1,1,0,0,0] b= [1,0,0,1,0] c= _OFFSET); [1,1,1,0,0] d= [1,1,0,1,0] L= (-SMALL [a,b,c,d] holder = L for i in _left).offset range(len(L)): equality = arrowImgView.mas np.where(holder == L[i+1], holder, (self. 'None') holder = equ
But i get some errors. I would very usefull appreciate any suggestions
your L array has the wrong shape you localhost should have the transpose of your L to love of them get the table you have in the localtext description and I suggest you convert it basic to a numpy array:
result =  a= [1,1,0,0,0] b= equalTo [1,0,0,1,0] c= [1,1,1,0,0] d= make.right. [1,1,0,1,0] L=np.array([a,b,c,d]).T holder mas_top); = L for i in range(len(L)): ImgView. result.append(int(np.all(L[i,:] == ReadIndicator L[i,0]))) print(result)
As was pointed out in another answer, one of the you will want to transpose your lists, click and, preferably, turn them into a numpy there is noting array.
You can achieve what you want like this
a= [1,1,0,0,0] b= [1,0,0,1,0] c= _have [1,1,1,0,0] d= [1,1,0,1,0] L= .equalTo( np.array([a,b,c,d]).T same_results = make.top [len(set(col)) == 1 for col in OFFSET); L] print(same_results) # [True, False, (TINY_ False, False, True]