編集履歴

質問編集履歴

コードの修正

2017/05/23 11:57

投稿

mi56

スコア14

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@@ -14,216 +14,214 @@
 なぜreturnでo,1,2が返るのか教えていただきたいです。
+###該当のソースコード
+```Python
+from __future__ import division
+import math, random
+import matplotlib.image as mpimg
+import matplotlib.pyplot as plt
+from functools import reduce
+import re, math, random
+from collections import defaultdict, Counter
+def vector_add(v, w):
+    """adds two vectors componentwise"""
+    return [v_i + w_i for v_i, w_i in zip(v,w)]
+def vector_subtract(v, w):
+    """subtracts two vectors componentwise"""
+    return [v_i - w_i for v_i, w_i in zip(v,w)]
+def vector_sum(vectors):
+    return reduce(vector_add, vectors)
+def scalar_multiply(c, v):
+    return [c * v_i for v_i in v]
+def vector_mean(vectors):
+    """compute the vector whose i-th element is the mean of the
+    i-th elements of the input vectors"""
+    n = len(vectors)
+    return scalar_multiply(1/n, vector_sum(vectors))
+def dot(v, w):
+    """v_1 * w_1 + ... + v_n * w_n"""
+    return sum(v_i * w_i for v_i, w_i in zip(v, w))
+def sum_of_squares(v):
+    """v_1 * v_1 + ... + v_n * v_n"""
+    return dot(v, v)
+def magnitude(v):
+    return math.sqrt(sum_of_squares(v))
+def squared_distance(v, w):
+    return sum_of_squares(vector_subtract(v, w))
+def distance(v, w):
+    return math.sqrt(squared_distance(v, w))
+class KMeans:
+    """performs k-means clustering"""
+    def __init__(self, k):
+        self.k = k          # number of clusters
+        self.means = None   # means of clusters
+    def classify(self, input):
+        """return the index of the cluster closest to the input"""
+        return min(range(self.k),
+                   key=lambda i: squared_distance(input, self.means[i]))
+    def train(self, inputs):
+        self.means = random.sample(inputs, self.k)
+        assignments = None
+        while True:
+            new_assignments = map(self.classify, inputs)
+            if assignments == new_assignments:
+                return
+            assignments = new_assignments
+            for i in range(self.k):
+                i_points = [p for p, a in zip(inputs, assignments) if a == i]
+                # avoid divide-by-zero if i_points is empty
+                if i_points:
+                    self.means[i] = vector_mean(i_points)
+if __name__ == "__main__":
+    inputs = [[-14,-5],[13,13],[20,23],[-19,-11],[-9,-16],[21,27],[-49,15],[26,13],[-46,5],[-34,-1],[11,15],[-49,0],[-22,-16],[19,28],[-12,-8],[-13,-19],[-41,8],[-11,-6],[-25,-9],[-18,-3]]
+    random.seed(0) # so you get the same results as me
+    clusterer = KMeans(3)
+    try:
+        clusterer.train(inputs)
+    except:
+        val =+ 1
+    print ("3-means:")
+    print (clusterer.means)
+first,second = zip(*inputs)
+plt.scatter(first,second)
 ```
-###該当のソースコード
-```Python
-from __future__ import division
-import math, random
-import matplotlib.image as mpimg
-import matplotlib.pyplot as plt
-from functools import reduce
-import re, math, random
-from collections import defaultdict, Counter
-def vector_add(v, w):
-    """adds two vectors componentwise"""
-    return [v_i + w_i for v_i, w_i in zip(v,w)]
-def vector_subtract(v, w):
-    """subtracts two vectors componentwise"""
-    return [v_i - w_i for v_i, w_i in zip(v,w)]
-def vector_sum(vectors):
-    return reduce(vector_add, vectors)
-def scalar_multiply(c, v):
-    return [c * v_i for v_i in v]
-def vector_mean(vectors):
-    """compute the vector whose i-th element is the mean of the
-    i-th elements of the input vectors"""
-    n = len(vectors)
-    return scalar_multiply(1/n, vector_sum(vectors))
-def dot(v, w):
-    """v_1 * w_1 + ... + v_n * w_n"""
-    return sum(v_i * w_i for v_i, w_i in zip(v, w))
-def sum_of_squares(v):
-    """v_1 * v_1 + ... + v_n * v_n"""
-    return dot(v, v)
-def magnitude(v):
-    return math.sqrt(sum_of_squares(v))
-def squared_distance(v, w):
-    return sum_of_squares(vector_subtract(v, w))
-def distance(v, w):
-    return math.sqrt(squared_distance(v, w))
-class KMeans:
-    """performs k-means clustering"""
-    def __init__(self, k):
-        self.k = k          # number of clusters
-        self.means = None   # means of clusters
-    def classify(self, input):
-        """return the index of the cluster closest to the input"""
-        return min(range(self.k),
-                   key=lambda i: squared_distance(input, self.means[i]))
-    def train(self, inputs):
-        self.means = random.sample(inputs, self.k)
-        assignments = None
-        while True:
-            new_assignments = map(self.classify, inputs)
-            if assignments == new_assignments:
-                return
-            assignments = new_assignments
-            for i in range(self.k):
-                i_points = [p for p, a in zip(inputs, assignments) if a == i]
-                # avoid divide-by-zero if i_points is empty
-                if i_points:
-                    self.means[i] = vector_mean(i_points)
-if __name__ == "__main__":
-    inputs = [[-14,-5],[13,13],[20,23],[-19,-11],[-9,-16],[21,27],[-49,15],[26,13],[-46,5],[-34,-1],[11,15],[-49,0],[-22,-16],[19,28],[-12,-8],[-13,-19],[-41,8],[-11,-6],[-25,-9],[-18,-3]]
-    random.seed(0) # so you get the same results as me
-    clusterer = KMeans(3)
-    try:
-        clusterer.train(inputs)
-    except:
-        val =+ 1
-    print ("3-means:")
-    print (clusterer.means)
-first,second = zip(*inputs)
-plt.scatter(first,second)
-```
 ###