The multinomial distribution represents the probability of observing multiple possible outcomes in a single trial. By selecting these specific rows, we can analyze and draw conclusions about the underlying probability distribution. This article involves choosing specific rows from a dataset that are generated from a multinomial distribution with ‘n’ degrees, which can be done by using the following methods:
1. Using “np.all()”:
Here, the np.all() function is used to check if all elements of each row of X are integers, by checking if the modulus of each element with 1 is zero. The axis=1 argument indicates that we want to apply this check to each row. The & operator is used to perform an element-wise “and” operation between the two boolean arrays: the one obtained from the integer check, and the one obtained from the sum check. Finally, we print the rows of X that satisfy both conditions by indexing X with the boolean array M.
2. Using “np.sum()” and “np.astype()”:
This code first converts all values in X to integers using the np.astype() method and checks if all values in each row are integers by comparing with the original X using ==. This is done using the np.sum() method along the rows (axis=1) and comparing the result with X.shape[1]. Next, the code checks if the sum of each row of X is equal to n using the np.sum() method along the rows (axis=1) and compares the result with n. Finally, the code combines the two conditions using logical AND (np.logical_and()) and prints the rows of X that satisfy both conditions.