![]() But when I try to train SVC model I obtain the ValueError: setting an array element with a sequence. that fit() method allows you to use: X : array-like or sparse matrix, shape(nsamples, nfeatures) Share. Here is an example of what I have already tried: import pandas as pd. In some cases it will create a object dtype array. If the elements vary in size it can't do that. Np.array(.) tries to create as high a dimensional array as it can. In : data = np.array() If I remove that, it can create an array. ![]() ValueError: setting an array element with a sequence.Īll your sub arrays have single items, but the last has 2. ValueError Traceback (most recent call last) So it has to first convert your list to an array. Is there any other way to solve this problem? Think.Np.save saves arrays, not lists. Why incrementing the loop by 2 help to reduce the total number of comparsion ? Why min and max are initialized differently for even and odd sized arrays? Time Complexity is O(n) and Space Complexity is O(1).įor each pair, there are a total of three comparisons, first among the elements of the pair and the other two with min and max. Binary text classification with TfidfVectorizer gives ValueError: setting an array element with a sequence 2 Getting 'ValueError: setting an array element with a sequence. By convention, we assume ans as max and ans as min The support set is represented as S (x1 RD is D-dimensional feature. We initialize both minimum and maximum element to the first element and then traverse the array, comparing each element and update minimum and maximum whenever necessary. The sequence of decimal values is stored in an array and considered as a pixel. ![]() Searching linearly: Increment the loop by 1 Searching linearly: Increment the loop by 1Ĭomparison in pairs: Increment the loop by 2ġ. ValueError: Expected 2D array, got 1D array instead: array0.33913043 0.36086956 0.4173913. You need to decrease the number of comparisons as much as you can. The bottleneck parameter in this problem is the number of comparisons that your algorithm requires to determine the maximum and the minimum element. The interviewer would not judge your algorithm for this question based on the time complexity as all solution has the time complexity of O(n). The input is related to the first 4 columns, the output is the last one. ![]() No, they can be positive, negative, or zero)Īre the array element sorted ? (Ans: No, they can be in any order) My dataframe is: 0 1 2 3 4 0 True, True, False 3 -1 False, True, True 1. Possible follow-up questions to ask the interviewer :-Īre the array elements necessarily positive? ( Your algorithm should make the minimum number of comparisons. Given an array A of size n, you need to find the maximum and minimum element present in the array. ![]()
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