create a class called DataSet. This class will have a default constructor that is empty, but an overloaded constructor that takes an integer argument and creates a vector with that many random numbers in it. For example, if I created:
DataSet d1 = new DataSet(500);
...it would create a vector inside that dataset with 500 randomly generated integers in it. Your DataSet class should also have functions that return the size of the dataset, the max number in the set, the min number in the set, and the average. You should not have any public member variables other than the string that names the dataset (pick whatever name you want for them...).
In your main code you will create three instances of that class and then analyze them. I want to see a list of the name, size, min, max, and avg of each dataset and I want this for dataset sizes of 10, 100, 1,000, and 10,000. The output should be simple but aligned.
For example:
Set1
10 1 9 4.5
100 0 98 49.2
etc., then Set2, then Set3.
Finally, I would like a cross-listed avg, so the same as above but only one list with the averages of all three sets data in it.
Draw a flowchart and write a C++ code for a program that reads two double numbers and then the program should calculate and display the sum and difference of the two numbers.
Apply the following format:
Set the width for printing to 10 and use & as a fill character.
The number of significant digits that be printed is 3
To create a class called `DataSet` in Python with the required functionalities, you can follow the steps below:
1. Import the necessary modules:
```python
import random
from statistics import mean
```
2. Define the `DataSet` class:
```python
class DataSet:
def __init__(self, size=0):
self.data = []
if size:
self.generate_data(size)
def generate_data(self, size):
self.data = [random.randint(0, 100) for _ in range(size)]
def get_size(self):
return len(self.data)
def get_min(self):
return min(self.data) if self.data else None
def get_max(self):
return max(self.data) if self.data else None
def get_avg(self):
return mean(self.data) if self.data else None
```
3. Create three instances of the `DataSet` class, each with different sizes:
```python
dataset1 = DataSet(10)
dataset2 = DataSet(100)
dataset3 = DataSet(1000)
```
4. Define a function `print_dataset_stats` to display the statistics of each dataset:
```python
def print_dataset_stats(dataset):
print(f"Size: {dataset.get_size()}\tMin: {dataset.get_min()}\tMax: {dataset.get_max()}\tAvg: {dataset.get_avg()}")
print("Set1")
print_dataset_stats(dataset1)
print("Set2")
print_dataset_stats(dataset2)
print("Set3")
print_dataset_stats(dataset3)
```
5. Create a list of all three datasets and calculate the cross-listed average:
```python
datasets = [dataset1, dataset2, dataset3]
cross_listed_avg = []
for dataset in datasets:
cross_listed_avg.append(dataset.get_avg())
print("Cross-Listed Avg:")
print_dataset_stats(DataSet(0)) # Create an empty dataset instance to display the average stats
print(cross_listed_avg)
```
The complete code would look like this:
```python
import random
from statistics import mean
class DataSet:
def __init__(self, size=0):
self.data = []
if size:
self.generate_data(size)
def generate_data(self, size):
self.data = [random.randint(0, 100) for _ in range(size)]
def get_size(self):
return len(self.data)
def get_min(self):
return min(self.data) if self.data else None
def get_max(self):
return max(self.data) if self.data else None
def get_avg(self):
return mean(self.data) if self.data else None
def print_dataset_stats(dataset):
print(f"Size: {dataset.get_size()}\tMin: {dataset.get_min()}\tMax: {dataset.get_max()}\tAvg: {dataset.get_avg()}")
dataset1 = DataSet(10)
dataset2 = DataSet(100)
dataset3 = DataSet(1000)
print("Set1")
print_dataset_stats(dataset1)
print("Set2")
print_dataset_stats(dataset2)
print("Set3")
print_dataset_stats(dataset3)
datasets = [dataset1, dataset2, dataset3]
cross_listed_avg = []
for dataset in datasets:
cross_listed_avg.append(dataset.get_avg())
print("Cross-Listed Avg:")
print_dataset_stats(DataSet(0))
print(cross_listed_avg)
```
Executing this code will generate three datasets with randomized numbers, display their statistics, and calculate the cross-listed average.