Feat/mateo pythonclass

docs/ClassPerson.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3e860c77-1efe-4bc5-a4dd-f6dda52f49d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Person:\n",
+    "    def __init__(self, givenName, givenColor, givenWeight):\n",
+    "        self.name = givenName\n",
+    "        self.color = givenColor\n",
+    "        self.weight = givenWeight\n",
+    "    def introduce_yourself(self):\n",
+    "        print(\"My name is \" + self.name)\n",
+    "    def sit_down(self):\n",
+    "        self.isSitting = true\n",
+    "    def stand_up(self):\n",
+    "        sel.isSitting = false"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "59231686-ea98-4d80-9e56-e2cdf79a264f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My name is Mateo\n"
+     ]
+    }
+   ],
+   "source": [
+    "mateo = Person(\"Mateo\", \"white\", 65)\n",
+    "mateo.introduce_yourself()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6781ce28-7295-4c58-ae5d-6980580a1e11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "int"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2366135b-11af-4faf-9960-d90ada0d6d3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bool"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8b625d8c-5f0e-46e0-9401-30dbff8158e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A is greater than b\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = 3\n",
+    "b = 1\n",
+    "if(a>b):\n",
+    "    print(\"A is greater than b\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1464218-f3ae-42ab-84f3-c4151e86ea4b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/jupyter.md

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+step by step basic command for jupyter lab
+1. Install jupyter:
+- pip install jupyterlab
+- jupyter lab
+
+2. Install jupyter notebook
+- pip install notebook
+- jupyter notebook

src/mateo/collections.py

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+# Named tuple example
+from collections import namedtuple
+Point = namedtuple('Point', ['x', 'y'])
+p = Point(100, 100)
+print(f"Point coordinates: ({p.x}, {p.y})")
+
+# Dataclasses example
+
+from dataclasses import dataclass
+
+@dataclass
+class PointData:
+    x: int
+    y: int
+p_data = PointData(200, 200)
+print(f"PointData coordinates: ({p_data.x}, {p_data.y})")
+
+# Ordered dict example
+from collections import OrderedDict
+
+od = OrderedDict()
+od['first'] = 1
+od['second'] = 2
+od['third'] = 3
+
+for key, value in od.items():
+    print(f"{key}: {value}")
+
+# Default dict example
+from collections import defaultdict
+dd = defaultdict(list)
+dd['a'].append(1)
+dd['b'].append(2)
+dd['a'].append(3)
+for key, value in dd.items():
+    print(f"{key}: {value}")
+
+# Deque example
+from collections import deque
+dq = deque(['a', 'b', 'c'])
+dq.append('d')
+dq.appendleft('z')
+print(f"Deque contents: {list(dq)}")
+

src/mateo/gaussian_distribution.py

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+import numpy as np
+from scipy.cluster.vq import kmeans, whiten
+import matplotlib.pyplot as plt
+import seaborn as sns
+sns.set(style="whitegrid")
+
+pts = 10
+rands = np.random.randint(0, 100, (10, 2))
+# Generate two clusters of points
+print(rands)
+a = np.random.multivariate_normal([0, 0], [[1, 0], [0, 1]], pts)
+b = np.random.multivariate_normal([5, 5], [[1, 0], [0, 1]], pts)
+
+features = np.concatenate((a, b), axis=0)
+
+whitened_features = whiten(features)
+
+codebook = kmeans(whitened_features, 2)
+
+plt.scatter(features[:, 0], features[:, 1], c='blue', label='Data Points')
+plt.scatter(codebook[0][:, 0], codebook[0][:, 1], c='red', marker='x', s=100, label='Centroids')
+plt.show()

src/mateo/kent.py

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+numbers = [None for _ in range(5)]
+print(numbers)
+
+CASHE_SIZE = 16
+_cache = [None for _ in range(CASHE_SIZE)]
+print(_cache)
+
+rows, cols = 5, 5
+grid = [['x' for _ in range(cols)] for _ in range(rows)]
+print(grid)
+
+for row in range(rows):
+    temp_row = []
+    for col in range(cols):
+        temp_row.append('o' if col > row else 'x')
+    grid.append(temp_row)
+print(grid)
+
+
+# dictionary comprehension
+

src/mateo/linear_regression.py

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+import numpy as np
+from sklearn.linear_model import LinearRegression
+
+x = np.array([5, 15, 25, 35, 45, 55]).reshape(-1, 1)
+y = np.array([5, 20, 14, 32, 22, 38])
+model = LinearRegression()
+
+model.fit(x, y)
+predictions = model.predict(x)
+
+r_squared = model.score(x, y)
+
+print(f"Coefficients: {r_squared}")
+
+y_pred = model.predict(x)
+print(f"Predictions: {y_pred}")

src/mateo/unionfind.py

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+class UnionFind:
+    def __init__(self, n):
+        self.parent = [i for i in range(n)]
+        self.rank = [0] * n
+    def find(self, x):
+        if(self.parent[x] != x):
+            self.parent[x] = self.find(self.parent[x])
+        return self.parent[x]
+    def union(self, x, y):
+        root_x = self.find(x)
+        root_y = self.find(y)
+        if(root_x == root_y):
+            return False
+        if root_x != root_y:
+            if self.rank[root_x] > self.rank[root_y]:
+                self.parent[root_y] = root_x
+            elif self.rank[root_x] < self.rank[root_y]:
+                self.parent[root_x] = root_y
+            else:
+                self.parent[root_y] = root_x
+                self.rank[root_x] += 1
+        return True
+u = UnionFind(10)
+
+print(u.union(1, 2))  # True
+print(u.union(2, 3))  # True
+print(u.union(1, 3))  # False
+print(u.find(1)) 
+print(u.find(2))