Initial release

Un programme qui apprend de ses erreurs.py

@@ -0,0 +1,52 @@
+import time
+import requests
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import train_test_split
+
+GET = "https://pydefis.callicode.fr/defis/MachineLearning/intern/C0MfHAoFXBILF1IiEgUCC0BVX14VE1FfTU5RVV9DAgQAUVdMaWtqGkJRXUBaWR4QHABGT0BEWDEeEABqXEEoKEBVBx4ABUVNU0ZdQgAFbBtQUy5fTTdpHQUGXlJERkFMBwQfD0dULl9NX28cEWwrTEJHQ10ABR0XXVZCXV5bB20dF0Q8X1M2NwMBABlGU0ZfTUEGBh8PR1QuX01ZbxwRbCtMQkBDXwUFHRdIVl1BWDEeEANqXEEoKEBaHAUdF0NUXUVfWW8cEQQtPA4%3D/getpoint"
+GET2 = "https://pydefis.callicode.fr/defis/MachineLearning/intern/C0MfHAoFXBILF1IiEgUCC0BVX14VE1FfTU5RVV9DAgQAUVdMaWtqGkJRXUBaWR4QHABGT0BEWDEeEABqXEEoKEBVBx4ABUVNU0ZdQgAFbBtQUy5fTTdpHQUGXlJERkFMBwQfD0dULl9NX28cEWwrTEJHQ10ABR0XXVZCXV5bB20dF0Q8X1M2NwMBABlGU0ZfTUEGBh8PR1QuX01ZbxwRbCtMQkBDXwUFHRdIVl1BWDEeEANqXEEoKEBaHAUdF0NUXUVfWW8cEQQtPA4%3D/reponse/"
+
+
+
+solution = 999
+
+while solution > 0:
+
+    dataset = pd.read_csv("MachineLearning_data.txt", names=['a', 'b', 'c'])
+
+    X = dataset.iloc[:, :-1].values
+    y = dataset.iloc[:, 2].values
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.999)
+
+    classifier = KNeighborsClassifier(n_neighbors=5)
+    classifier.fit(X_train, y_train)
+
+    # y_pred = classifier.predict(X_test)
+
+    res = requests.get(GET)
+    a,b = [float(v) for v in res.text.replace("[", "").replace("]", "").split(",")]
+
+    X = [[a,b], ]
+    y = classifier.predict(X)
+
+    color = y[0]
+
+    print(a, b, color, ": ", end="")
+
+    res = requests.get(GET2 + str(color))
+    solution = int(res.text.strip())
+    if solution == color:
+        print("OK")
+    else:
+        print("NOK ({})".format(solution))
+
+    datafile = open("MachineLearning_data.txt", "a")
+    datafile.write("{},{},{}\n".format(a,b,solution))
+    datafile.close
+
+    time.sleep(1)