Merge pull request #1 from jetbrains-academy/sofia/random

Sofia/random

Author: sofiiako

Numpy/Array Basics/Random Sampling/__init__.py

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+type: edu
+files:
+- name: task.py
+  visible: true
+  placeholders:
+  - offset: 58
+    length: 23
+    placeholder_text: '# TODO'
+  - offset: 137
+    length: 27
+    placeholder_text: '# TODO'
+  - offset: 95
+    length: 6
+    placeholder_text: '# TODO'
+- name: tests/test_task.py
+  visible: false
+- name: __init__.py
+  visible: false
+- name: tests/__init__.py
+  visible: false

Numpy/Array Basics/Random Sampling/task-info.yaml

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+## Random Sampling
+
+Sometimes you might need to fill an array with random numbers or sample them from 
+different statistical distributions. 
+Numpy's `random` module allows you to do this. It is a suite of functions based on 
+pseudorandom number generation. It is called pseudorandom because "random" means something that 
+can not be predicted logically, and if a program generates a "random" number 
+it can be predicted, thus it is not truly random.
+
+The function `numpy.random.random` returns random floats in the half-open interval `[0.0, 1.0)`:
+
+```python
+a = np.random.random(5)
+print(a)
+```
+Output:
+```text
+[0.73719247 0.38265166 0.48330973 0.27551432 0.88136674]
+```
+
+### Random Generator
+Call [`default_rng`](https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.default_rng) 
+to get a new instance of a [`Generator`](https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator), 
+then call its methods to obtain samples from different distributions. 
+```python
+rng = np.random.default_rng()
+a = rng.integers(1000)  # Randomly pick one integer from 0 to 1000
+print(a)
+```
+Output:
+```text
+346
+```
+`random.Generator.integers` returns random integers from a half-open interval: low (inclusive) to high (exclusive), 
+or if you specify `endpoint=True`, then the interval is closed: low (inclusive) to high (inclusive).
+Integers are returned from the "discrete uniform" distribution.
+
+Print 10 random integers from the interval `[0, 2)`:
+
+```python
+print(rng.integers(2, size=10)) 
+```
+Output:
+```text
+[1 1 0 1 0 0 0 1 0 0]
+```
+Generate a 2 x 4 array of integers between `0` and `4`, inclusive:
+```python
+print(rng.integers(4, size=(2, 4), endpoint=True))
+```
+Equivalent to:
+```python
+print(rng.integers(5, size=(2, 4)))
+```
+Output:
+```text
+[[1 2 0 3]
+ [4 2 4 3]]
+```
+
+### Task
+Using the function [`numpy.random.Generator.normal`](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.normal.html?highlight=random%20normal#numpy.random.Generator.normal), 
+draw 1000 samples from the normal distribution with mean equal to `1` and standard deviation equal to `0.5`.
+You can visualize your sample and make sure the mean and variance are ok by running the script – we predefined the 
+code for that in the `if __name__ == '__main__':` block.

Numpy/Array Basics/Random Sampling/task.md

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+import numpy as np
+import matplotlib.pyplot as plt
+
+rng = np.random.default_rng()
+
+mu, sigma = 1, 0.5  # Mean and standard deviation
+s = rng.normal(mu, sigma, 1000)
+
+
+if __name__ == '__main__':
+    # Verify the mean and the variance:
+    print(abs(mu - np.mean(s)))  # Difference should be close to 0.0
+    print(abs(sigma - np.std(s, ddof=1)))  # Difference should be close to 0.0
+
+    count, bins, ignored = plt.hist(s, 30, density=True)
+    plt.plot(bins, 1 / (sigma * np.sqrt(2 * np.pi)) *
+             np.exp(- (bins - mu) ** 2 / (2 * sigma ** 2)),
+             linewidth=2, color='r')
+    plt.show()
+

Numpy/Array Basics/Random Sampling/task.py

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+import unittest
+import numpy as np
+
+from task import *
+
+
+class TestCase(unittest.TestCase):
+    def test_normal(self):
+        self.assertEqual(s.shape, (1000,), msg="Draw 1000 samples")
+        self.assertEqual(round(abs(mu - np.mean(s)), 1), 0.0,  msg="Mean should be close to 0.0")
+        self.assertEqual(round(abs(sigma - np.std(s, ddof=1)), 1), 0.0,  msg="Variance should be close to 0.0")
+

Numpy/Array Basics/Random Sampling/tests/__init__.py

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+type: edu
+files:
+- name: task.py
+  visible: true
+  placeholders:
+  - offset: 24
+    length: 20
+    placeholder_text: '# TODO'
+  - offset: 49
+    length: 15
+    placeholder_text: '# TODO'
+- name: tests/test_task.py
+  visible: false
+- name: __init__.py
+  visible: false
+- name: tests/__init__.py
+  visible: false

Numpy/Array Basics/Random Sampling/tests/test_task.py

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+## Reshape
+
+Reshaping means changing the `shape` of an array without changing its data.
+As we mentioned earlier, the `shape` of an array is the number of elements in each dimension.
+By reshaping we can add or remove dimensions or change the number of elements in each dimension.
+
+The function [`numpy.reshape`](https://numpy.org/doc/stable/reference/generated/numpy.reshape.html#numpy.reshape) 
+is used for reshaping, and it accepts three arguments:
+
+- The array to be reshaped.
+- The new shape as an `int` or tuple of `int`s.
+- An optional argument `order`, which defines the order in which the elements are read and placed into the reshaped array.
+
+### Examples
+1. Reshape 1D array into a 2D array:
+```python
+a = np.arange(10)
+print(np.reshape(a, (2, 5)))
+```
+Output:
+```text
+[[0 1 2 3 4]
+ [5 6 7 8 9]]
+```
+2. Reshape a 2D array into a 1D array:
+```python
+a = np.array([[1, 2, 3], [4, 5, 6]])
+print(np.reshape(a, 6))
+```
+Output:
+```text
+[1 2 3 4 5 6]
+```
+
+The latter can also be achieved by using `numpy.ndarray.flatten`:
+```python
+a = np.array([[1, 2, 3], [4, 5, 6]])
+print(a.flatten())
+```
+Output:
+```text
+[1 2 3 4 5 6]
+```
+
+### Task
+1. Create an array `a` of integers in the interval from `12` to `30` with step `3`.
+2. Reshape it so that it has 2 rows and 3 columns.
+
+<div class="hint">For (1) you can use the <code>arange</code> function.</div>

Numpy/Array Basics/Reshape/__init__.py

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+import numpy as np
+
+a = np.arange(12, 30, 3)
+b = a.reshape(2, 3)
+
+if __name__ == "__main__":
+    print(a)
+    print('shape : ', a.shape)
+    print('\nAfter reshaping : ')
+    print(b)
+    print('shape : ', b.shape)