Copied qml_200_WhoLikesTheBeatles problem templates

Coding_Challenges/qml_200_WhoLikesTheBeatles_template/1.ans

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+0,0.4210528986556

Coding_Challenges/qml_200_WhoLikesTheBeatles_template/1.in

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+23,150,1,13,20,YES,24,200,YES,14,130,NO,50,20,NO

Coding_Challenges/qml_200_WhoLikesTheBeatles_template/2.ans

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+0,0.9264606253589677

Coding_Challenges/qml_200_WhoLikesTheBeatles_template/2.in

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+23,10,1,20,100,YES,25,20,YES,14,130,NO,50,20,YES,60,300,YES,45,30,NO,33,20,NO

Coding_Challenges/qml_200_WhoLikesTheBeatles_template/who_likes_the_beatles_template.py

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+#! /usr/bin/python3
+
+import sys
+from pennylane import numpy as np
+import pennylane as qml
+
+
+def distance(A, B):
+    """Function that returns the distance between two vectors.
+
+    Args:
+        - A (list[int]): person's information: [age, minutes spent watching TV].
+        - B (list[int]): person's information: [age, minutes spent watching TV].
+
+    Returns:
+        - (float): distance between the two feature vectors.
+    """
+
+    # QHACK #
+
+    # The Swap test is a method that allows you to calculate |<A|B>|^2 , you could use it to help you.
+    # The qml.AmplitudeEmbedding operator could help you too.
+
+    # dev = qml.device("default.qubit", ...
+    # @qml.qnode(dev)
+
+    # QHACK #
+
+
+def predict(dataset, new, k):
+    """Function that given a dataset, determines if a new person do like Beatles or not.
+
+    Args:
+        - dataset (list): List with the age, minutes that different people watch TV, and if they like Beatles.
+        - new (list(int)): Age and TV minutes of the person we want to classify.
+        - k (int): number of nearby neighbors to be taken into account.
+
+    Returns:
+        - (str): "YES" if they like Beatles, "NO" otherwise.
+    """
+
+    # DO NOT MODIFY anything in this code block
+
+    def k_nearest_classes():
+        """Function that returns a list of k near neighbors."""
+        distances = []
+        for data in dataset:
+            distances.append(distance(data[0], new))
+        nearest = []
+        for _ in range(k):
+            indx = np.argmin(distances)
+            nearest.append(indx)
+            distances[indx] += 2
+
+        return [dataset[i][1] for i in nearest]
+
+    output = k_nearest_classes()
+
+    return (
+        "YES" if len([i for i in output if i == "YES"]) > len(output) / 2 else "NO",
+        float(distance(dataset[0][0], new)),
+    )
+
+
+if __name__ == "__main__":
+    # DO NOT MODIFY anything in this code block
+    inputs = sys.stdin.read().split(",")
+    dataset = []
+    new = [int(inputs[0]), int(inputs[1])]
+    k = int(inputs[2])
+    for i in range(3, len(inputs), 3):
+        dataset.append([[int(inputs[i + 0]), int(inputs[i + 1])], str(inputs[i + 2])])
+
+    output = predict(dataset, new, k)
+    sol = 0 if output[0] == "YES" else 1
+    print(f"{sol},{output[1]}")