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Author: ashishkumar30

numpy.py

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+import numpy as np
+
+
+'''
+#Q1.Create a numpy array with 10 elements of the shape(10,1) using np.random and find out the mean of the elements using basic numpy functions.
+
+
+array=np.random.rand(10,1)
+print("Array:\n",array)
+print("Mean: ",array.mean(axis=0))
+
+'''
+
+
+
+'''
+#Q2.Create a numpy array with 20 elements of the shape(20,1) using np.random find the variance and standard deviation of the elements. 
+
+array=np.random.rand(20,1)
+print("Array:\n",array)
+
+print("Mean: ",array.mean(axis=0))
+print("Variance: ",array.var(axis=0))
+print("Standar Deviation: ",array.std(axis=0))
+
+'''
+
+
+
+
+'''
+#Q3.Create a numpy array A of shape(10,20) and B of shape (20,25) using np.random. Print the matrix which is the matrix multiplication of A and B. The shape of the new matrix should be (10,25). Using basic numpy math functions only find the sum of all the elements of the new matrix.
+
+x=np.random.rand(10,20)
+y=np.random.rand(20,25)
+
+z=x.dot(y)
+print(z)
+print("Shape of new array: ",np.shape(z))
+print("Sum of elements in new array: ",np.sum(z))
+'''
+
+
+
+'''
+#Q4.Create a numpy array A of shape(10,1).Using the basic operations of the numpy array generate an array of shape(10,1) such that each element is the following function applied on each element of A.
+#f(x)=1 / (1 + exp(-x)) 
+#Apply this function to each element of A and print the new array holding the value the function returns
+#Example:
+#a=[a1,a2,a3]   
+#n(new array to be printed )=[ f(a1), f(a2), f(a3)]
+
+
+
+A=np.random.rand(10,1)
+print("\nArray A:\n",A)
+
+f=lambda x:(1/(1+np.exp(-x)))
+
+print("\nNew array:\n",np.array(list(map(f,A))))
+
+'''