Your objective is to construct the trace table (or list) for the caller F(196) where F() is defined as follows bool F(int n) { int o, t, h, r; O -n % 10; t -n / 10 % 10; h = n / 100 % 10; r - 100 K(h,3); r +- 10 K(t,2); r += K(o,1); n = 100 h + 10 t + o; bool s = (r - n > 0); return !s;

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Your objective is to construct the trace table (or list) for the caller F(196) where F() is defined as follows

Your objective is to construct the trace table (or list) for the caller F(196) where F() is defined as follows:

```cpp
bool F(int n)
{
    int o, t, h, r;
    o = n % 10;
    t = n / 10 % 10;
    h = n / 100 % 10;
    r = 100 * K(h, 3);
    r += 10 * K(t, 2);
    r += K(o, 1);
    n = 100 * h + 10 * t + o;
    bool s = (r - n > 0);
    return !s;
}
```

where K() is defined as follows:

```cpp
int K(int& n, int e)
{
    n = (e * n + e) % 10;
    return ((n * n + (10 - e)) % 10);
}
```

**Warning:** Do not write any trace table for K(). You will receive a 0 if you do so.
Transcribed Image Text:Your objective is to construct the trace table (or list) for the caller F(196) where F() is defined as follows: ```cpp bool F(int n) { int o, t, h, r; o = n % 10; t = n / 10 % 10; h = n / 100 % 10; r = 100 * K(h, 3); r += 10 * K(t, 2); r += K(o, 1); n = 100 * h + 10 * t + o; bool s = (r - n > 0); return !s; } ``` where K() is defined as follows: ```cpp int K(int& n, int e) { n = (e * n + e) % 10; return ((n * n + (10 - e)) % 10); } ``` **Warning:** Do not write any trace table for K(). You will receive a 0 if you do so.
Expert Solution
Step 1 The NumPy ndarray: A Multidimensional Array Object

One of the key capabilities of NumPy is its N-dimensional array object, or ndarray, which is a fast, flexible container for massive records units in Python. Arrays enable you to carry out mathematical operations on entire blocks of information the use of similar syntax to the equivalent operations among scalar factors:

In [8]: facts
Out[8]: 
array([[ 0.9526, -0.246 , -0.8856],
       [ 0.5639,  0.2379,  0.9104]])

In [9]: facts * 10                         In [10]: records + statistics                
Out[9]:                                   Out[10]:                            
array([[ 9.5256, -2.4601, -8.8565],       array([[ 1.9051, -0.492 , -1.7713], 
       [ 5.6385,  2.3794,  9.104 ]])             [ 1.1277,  0.4759,  1.8208]])
An ndarray is a general multidimensional container for homogeneous statistics; that is, all the factors should be the identical kind. Every array has a form, a tuple indicating the size of each dimension, and a dtype, an item describing the information kind of the array:

In [11]: data.Shape
Out[11]: (2, 3)

In [12]: statistics.Dtype
Out[12]: dtype('float64')
This bankruptcy will introduce you to the fundamentals of using NumPy arrays, and ought to be enough for following in conjunction with the rest of the e book. While it’s no longer vital to have a deep information of NumPy for plenty facts analytical programs, becoming proficient in array-oriented programming and wondering is a key step along the way to turning into a systematic Python guru.

NOTE
Whenever you notice “array”, “NumPy array”, or “ndarray” within the text, with few exceptions all of them discuss with the identical factor: the ndarray item.

Creating ndarrays
The simplest manner to create an array is to apply the array feature. This accepts any collection-like object (including different arrays) and produces a brand new NumPy array containing the handed records. For example, a list is a superb candidate for conversion:

In [13]: data1 = [6, 7.5, 8, 0, 1]

In [14]: arr1 = np.Array(data1)

In [15]: arr1
Out[15]: array([ 6. ,  7.5,  8. ,  0. ,  1. ])
Nested sequences, like a list of same-duration lists, might be converted into a multidimensional array:

In [16]: data2 = [[1, 2, 3, 4], [5, 6, 7, 8]]

In [17]: arr2 = np.Array(data2)

In [18]: arr2
Out[18]: 
array([[1, 2, 3, 4],
       [5, 6, 7, 8]])

In [19]: arr2.Ndim
Out[19]: 2

In [20]: arr2.Shape

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