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implements the constructor of the following class and the sparseness_metric method. Notice that the constructor should: - set the value of self.shape to the size of the matrix m. If m is None the size must be (0,0) - fill the dictionary self.rows as a dictionary of dictionaries with the non-0 values of m. You will need to iterate through the entire array m for this Example of execution (image)   def SparseMatrix2(m=None, **kwargs):          import itertools     def add_to_dict(d, key1, key2, val):              <... YOUR CODE HERE ...>         return d     class SparseMatrix2_class:         def __init__(self, m=m, **kwargs):             self.rows = {}             self.shape =  ...              <... YOUR CODE HERE ...>         def sparseness_metric(self):             metric = <... YOUR CODE HERE ...>             return metric          return SparseMatrix2_class(**kwargs)   manually check your code with: import numpy as np def random_sparse_matrix(size):     m = np.random.randint(2, size=size)     m = m * np.random.randint(10,size=size)     return m m = random_sparse_matrix((5,3)) sm = SparseMatrix2(m) print(m.shape, sm.sparseness_metric()) m s = SparseMatrix2(m) print("rows", s.rows) print("length of each row", [len(s.rows[i]) for i in list(s.rows.keys())]) s = SparseMatrix2(None) s.shape

implements the constructor of the following class and the sparseness_metric method. Notice that the constructor should: - set the value of self.shape to the size of the matrix m. If m is None the size must be (0,0) - fill the dictionary self.rows as a dictionary of dictionaries with the non-0 values of m. You will need to iterate through the entire array m for this Example of execution (image)   def SparseMatrix2(m=None, **kwargs):          import itertools     def add_to_dict(d, key1, key2, val):              <... YOUR CODE HERE ...>         return d     class SparseMatrix2_class:         def __init__(self, m=m, **kwargs):             self.rows = {}             self.shape =  ...              <... YOUR CODE HERE ...>         def sparseness_metric(self):             metric = <... YOUR CODE HERE ...>             return metric          return SparseMatrix2_class(**kwargs)   manually check your code with: import numpy as np def random_sparse_matrix(size):     m = np.random.randint(2, size=size)     m = m * np.random.randint(10,size=size)     return m m = random_sparse_matrix((5,3)) sm = SparseMatrix2(m) print(m.shape, sm.sparseness_metric()) m s = SparseMatrix2(m) print("rows", s.rows) print("length of each row", [len(s.rows[i]) for i in list(s.rows.keys())]) s = SparseMatrix2(None) s.shape

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implements the constructor of the following class and the sparseness_metric method. Notice that the constructor should:

- set the value of self.shape to the size of the matrix m. If m is None the size must be (0,0)

- fill the dictionary self.rows as a dictionary of dictionaries with the non-0 values of m. You will need to iterate through the entire array m for this

Example of execution (image)

 

def SparseMatrix2(m=None, **kwargs):
    
    import itertools

    def add_to_dict(d, key1, key2, val):
    
        <... YOUR CODE HERE ...>
        return d

    class SparseMatrix2_class:

        def __init__(self, m=m, **kwargs):
            self.rows = {}
            self.shape =  ... 
            <... YOUR CODE HERE ...>

        def sparseness_metric(self):
            metric = <... YOUR CODE HERE ...>
            return metric
    
    return SparseMatrix2_class(**kwargs)

 

manually check your code with:

import numpy as np

def random_sparse_matrix(size):
    m = np.random.randint(2, size=size)
    m = m * np.random.randint(10,size=size)
    return m

m = random_sparse_matrix((5,3))

sm = SparseMatrix2(m)
print(m.shape, sm.sparseness_metric())
m
s = SparseMatrix2(m)
print("rows", s.rows)
print("length of each row", [len(s.rows[i]) for i in list(s.rows.keys())])
s = SparseMatrix2(None)
s.shape

> m= array([[1, 0, 9], [0, 0, 7], > [3, 5, 0], [5, 0, 0], [0, 3, 0]]) > s = SparseMatrix (m) > print "rows", s.rows > print "length of each row", [len(s.rows[i]) for i in s.rows.keys()] > print "sparseness metric", s.sparseness_metric() rows {0: {0: 1, 2: 9), 1: {2: 7), 2: {0: 3, 1: 5), 3: {0: 5}, 4: {1: 3}} length of each row [2, 1, 2, 1, 1] sparseness metric 0.3333333
Transcribed Image Text:> m= array([[1, 0, 9], [0, 0, 7], > [3, 5, 0], [5, 0, 0], [0, 3, 0]]) > s = SparseMatrix (m) > print "rows", s.rows > print "length of each row", [len(s.rows[i]) for i in s.rows.keys()] > print "sparseness metric", s.sparseness_metric() rows {0: {0: 1, 2: 9), 1: {2: 7), 2: {0: 3, 1: 5), 3: {0: 5}, 4: {1: 3}} length of each row [2, 1, 2, 1, 1] sparseness metric 0.3333333
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