-

ia/tp3/main.py

@@ -0,0 +1,67 @@
+#!/bin/env -S python3
+
+from dataclasses import dataclass
+
+import numpy
+import pandas
+import random
+import math
+
+def main():
+    dataset = load_dataset("bin/iris/iris.data")
+    # print("dataset", dataset)
+    (dataset_training, dataset_test) = split_dataset(dataset, 0.8)
+    # print("dataset_training", dataset_training, "dataset_test", dataset_test)
+    centroid = KVMeans(3, 4)
+    centroid.learn(dataset_training[["a", "b", "c", "d"]])
+
+
+
+def load_dataset(path: str):
+    data = pandas.read_csv(path, names=["a", "b", "c", "d", "category"])
+    return data
+
+
+def split_dataset(dataset: pandas.DataFrame, ratio: float):
+    total_len = len(dataset)
+    start_len = math.floor(total_len * ratio)
+    (start, rest) = numpy.array_split(dataset, [start_len])
+    return (start, rest)
+
+
+@dataclass
+class Centroid:
+    dimension: int
+    position: list[float]
+
+    @staticmethod
+    def new_random(dimension: int, rng: random.Random):
+        return Centroid(dimension, [rng.random() for _ in range(dimension)])
+
+class KVMeans:
+
+    def __init__(self, mean_count: int, dimension: int):
+        rng = random.Random(0)
+        self.means = [Centroid.new_random(dimension, rng) for _ in range(mean_count)]
+        self.dimension = dimension
+        self.mean_count = mean_count
+
+    def learn(self, dataset: pandas.DataFrame):
+        assert dataset.columns.size == self.dimension
+        for (_, line) in dataset.iterrows():
+            for absc in range(self.dimension):
+                pass
+
+        # print([3 for _ in series])
+        # print(series)
+        # dataset.map(lambda e: print(e))
+
+    def predict(self):
+        pass
+
+def distance_dim(a: list[float], b: list[float]):
+    acc = 0
+    for (a_, b_) in zip(a, b): acc += math.pow(a_ - b_, 2)
+    return math.sqrt(acc)
+
+if __name__ == "__main__": main()