Merge remote-tracking branch 'upstream/master'

data_structures/Binary Tree/binary_seach_tree.py

@@ -8,7 +8,7 @@ class Node:
     def __init__(self, label):
         self.label = label
         self.left = None
-        self.rigt = None
+        self.right = None
 
     def getLabel(self):
         return self.label
@@ -23,10 +23,10 @@ def setLeft(self, left):
         self.left = left
 
     def getRight(self):
-        return self.rigt
+        return self.right
 
     def setRight(self, right):
-        self.rigt = right
+        self.right = right
 
 
 class BinarySearchTree:

Graphs/Breadth_First_Search.py → data_structures/Graph/Breadth_First_Search.py

@@ -1,9 +1,9 @@
 class GRAPH:
     """docstring for GRAPH"""
     def __init__(self, nodes):
-        self.nodes=nodes
-        self.graph=[[0]*nodes for i in range (nodes)]
-        self.visited=[0]*nodes
+        self.nodes = nodes
+        self.graph = [[0]*nodes for i in range (nodes)]
+        self.visited = [0]*nodes
 
 
     def show(self):
@@ -23,7 +23,7 @@ def bfs(self,v):
             v = queue[0]
             for u in range(self.vertex):
                 if self.graph[v][u] == 1:
-                    if visited[u]== False:
+                    if visited[u] is False:
                         visited[u] = True
                         queue.append(u)
                         print('%d visited' % (u +1))
@@ -41,30 +41,32 @@ def bfs(self,v):
 g.add_edge(5,9)
 g.add_edge(6,10)
 g.bfs(4)
-=======
-        print self.graph
+
+print(self.graph)
 
     def add_edge(self, i, j):
         self.graph[i][j]=1
         self.graph[j][i]=1
 
-    def bfs(self,s):
-        queue=[s]
-        self.visited[s]=1
-        while len(queue)!=0:
-            x=queue.pop(0)
+    def bfs(self, s):
+        queue = [s]
+        self.visited[s] = 1
+        while len(queue)!= 0:
+            x = queue.pop(0)
             print(x)
-            for i in range(0,self.nodes):
-                if self.graph[x][i]==1 and self.visited[i]==0:
+            for i in range(0, self.nodes):
+                if self.graph[x][i] == 1 and self.visited[i] == 0:
                     queue.append(i)     
-                    self.visited[i]=1
+                    self.visited[i] = 1
 
-n=int(input("Enter the number of Nodes : "))
-g=GRAPH(n)
-e=int(input("Enter the no of edges : "))
+n = int(input("Enter the number of Nodes : "))
+g = GRAPH(n)
+e = int(input("Enter the no of edges : "))
 print("Enter the edges (u v)")
-for i in range(0,e):
-    u,v=map(int, raw_input().split())
-    g.add_edge(u,v)
-s=int(input("Enter the source node :"))
+
+for i in range(0, e):
+    u ,v = map(int, raw_input().split())
+    g.add_edge(u, v)
+
+s = int(input("Enter the source node :"))
 g.bfs(s)

machine_learning/decision_tree.py

@@ -0,0 +1,139 @@
+"""
+Implementation of a basic regression decision tree.
+Input data set: The input data set must be 1-dimensional with continuous labels.
+Output: The decision tree maps a real number input to a real number output. 
+"""
+
+import numpy as np
+
+class Decision_Tree:
+    def __init__(self, depth = 5, min_leaf_size = 5):
+        self.depth = depth
+        self.decision_boundary = 0
+        self.left = None
+        self.right = None
+        self.min_leaf_size = min_leaf_size
+        self.prediction = None
+
+    def mean_squared_error(self, labels, prediction):
+        """
+        mean_squared_error:
+        @param labels: a one dimensional numpy array 
+        @param prediction: a floating point value
+        return value: mean_squared_error calculates the error if prediction is used to estimate the labels
+        """
+        if labels.ndim != 1:
+            print("Error: Input labels must be one dimensional")
+
+        return np.mean((labels - prediction) ** 2)
+
+    def train(self, X, y):
+        """
+        train:
+        @param X: a one dimensional numpy array
+        @param y: a one dimensional numpy array. 
+        The contents of y are the labels for the corresponding X values
+
+        train does not have a return value
+        """
+
+        """
+        this section is to check that the inputs conform to our dimensionality constraints
+        """
+        if X.ndim != 1:
+            print("Error: Input data set must be one dimensional")
+            return
+        if len(X) != len(y):
+            print("Error: X and y have different lengths")
+            return
+        if y.ndim != 1:
+            print("Error: Data set labels must be one dimensional")
+            return
+
+        if len(X) < 2 * self.min_leaf_size:
+            self.prediction = np.mean(y)
+            return
+
+        if self.depth == 1:
+            self.prediction = np.mean(y)
+            return
+
+        best_split = 0
+        min_error = self.mean_squared_error(X,np.mean(y)) * 2
+
+
+        """
+        loop over all possible splits for the decision tree. find the best split.
+        if no split exists that is less than 2 * error for the entire array
+        then the data set is not split and the average for the entire array is used as the predictor
+        """
+        for i in range(len(X)):
+            if len(X[:i]) < self.min_leaf_size:
+                continue
+            elif len(X[i:]) < self.min_leaf_size:
+                continue
+            else:
+                error_left = self.mean_squared_error(X[:i], np.mean(y[:i]))
+                error_right = self.mean_squared_error(X[i:], np.mean(y[i:]))
+                error = error_left + error_right
+                if error < min_error:
+                    best_split = i
+                    min_error = error
+
+        if best_split != 0:
+            left_X = X[:best_split]
+            left_y = y[:best_split]
+            right_X = X[best_split:]
+            right_y = y[best_split:]
+
+            self.decision_boundary = X[best_split]
+            self.left = Decision_Tree(depth = self.depth - 1, min_leaf_size = self.min_leaf_size)
+            self.right = Decision_Tree(depth = self.depth - 1, min_leaf_size = self.min_leaf_size)
+            self.left.train(left_X, left_y)
+            self.right.train(right_X, right_y)
+        else:
+            self.prediction = np.mean(y)
+
+        return
+
+    def predict(self, x):
+        """
+        predict:
+        @param x: a floating point value to predict the label of
+        the prediction function works by recursively calling the predict function
+        of the appropriate subtrees based on the tree's decision boundary
+        """
+        if self.prediction is not None:
+            return self.prediction
+        elif self.left or self.right is not None:
+            if x >= self.decision_boundary:
+                return self.right.predict(x)
+            else:
+                return self.left.predict(x)
+        else:
+            print("Error: Decision tree not yet trained")
+            return None
+
+def main():
+    """
+    In this demonstration we're generating a sample data set from the sin function in numpy.
+    We then train a decision tree on the data set and use the decision tree to predict the
+    label of 10 different test values. Then the mean squared error over this test is displayed.
+    """
+    X = np.arange(-1., 1., 0.005)
+    y = np.sin(X)
+
+    tree = Decision_Tree(depth = 10, min_leaf_size = 10)
+    tree.train(X,y)
+
+    test_cases = (np.random.rand(10) * 2) - 1
+    predictions = np.array([tree.predict(x) for x in test_cases])
+    avg_error = np.mean((predictions - test_cases) ** 2)
+
+    print("Test values: " + str(test_cases))
+    print("Predictions: " + str(predictions))
+    print("Average error: " + str(avg_error))
+
+
+if __name__ == '__main__':
+    main()

searches/binary_search.py

@@ -110,9 +110,9 @@ def binary_search_by_recursion(sorted_collection, item, left, right):
     if sorted_collection[midpoint] == item:
         return midpoint
     elif sorted_collection[midpoint] > item:
-        return binary_search_by_recursion(sorted_collection, item, left, right-1)
+        return binary_search_by_recursion(sorted_collection, item, left, midpoint-1)
     else:
-        return binary_search_by_recursion(sorted_collection, item, left+1, right)
+        return binary_search_by_recursion(sorted_collection, item, midpoint+1, right)
 
 def __assert_sorted(collection):
     """Check if collection is sorted, if not - raises :py:class:`ValueError`