setting an array element with a sequence rd.fit

#Setting an array element with a sequence rd.fit update#
#Setting an array element with a sequence rd.fit code#

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. The input is related to the first 4 columns, the output is the last one. By convention, we assume ans as max and ans as min My dataframe is: 0 1 2 3 4 0 True, True, False 3 -1 False, True, True 1.

#Setting an array element with a sequence rd.fit update#

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. Searching linearly: Increment the loop by 1 Searching linearly: Increment the loop by 1Ĭomparison in pairs: Increment the loop by 2ġ.

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).

No, they can be positive, negative, or zero)Īre the array element sorted ? (Ans: No, they can be in any order) Possible follow-up questions to ask the interviewer :-Īre the array elements necessarily positive? ( Your algorithm should make the minimum number of comparisons. Note make sure that Venue Categories column has same number of elements for each row of data, else a new problem will arise again.Given an array A of size n, you need to find the maximum and minimum element present in the array. Train_data,test_data,train_labels,test_labels = train_test_split(data,labels,test_size=0.20) Return np.array( for col in other_features]+categories for i in range(len(categories))]), np.array(df.values)ĭff = labelencoder.fit_transform(dff)ĭata, labels = make_data(dff, 'Venue', 'Venue Categories') Not converting the Numpy Array to a list, same error Making sure that each element in xtrain.vector has the same length - it has Making sure that each element in xtrain.vector is of the same type - done Converting xtrain. I managed to make it work, by combining the city column with the venue categories column into a 2D (numpy) array which can be used by the RandomForestClassifier of sklearn.įrom sklearn.preprocessing import LabelEncoderįrom sklearn.ensemble import RandomForestClassifierįrom sklearn.model_selection import train_test_splitĭef make_data(df, target_column='Venue', categories_column='Venue Categories'):Ĭategories = df.values dfcollect df.toPandas() features np. When you do dfcollectfeatures.apply(lambda x: np.array(x)).tonumpy() you get an array of arrays which is not the same as a 2D array.

#Setting an array element with a sequence rd.fit code#

This will looks weird, I want to make it global, means there should not need to touch the code if we may increase the number of columns. You need to convert this column of lists to a 2D array. I want to add more columns, then I again write everything for each feature just like shown below, if i want to add type column and owner column city = dff.valuesĬategories = dff.valuesĭata = np.array(, owner, type categories) for i in range(len(city))]) Let's say, if I want to increase the number of features. I am passing this data to my machine learning model, but model.fit is not accepting the input, My code is shown below, that I am trying, labelencoder = LabelEncoder()ĭff=labelencoder.fit_transform(dff) WaterFront Austria Īeronaut Marvilles Īeronaut Paris I have the columns in my Data Frame as shown below: Venue city Venue Categories