mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-11-28 07:21:07 +00:00
Merge remote-tracking branch 'upstream/master'
This commit is contained in:
Stephen Lee
commit
7c9a07c0a0
14
.travis.yml
14
.travis.yml
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@ -1,14 +0,0 @@
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language: python
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python:
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- "3.2"
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- "3.3"
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- "3.4"
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- "3.5"
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- "3.6"
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- "3.6-dev"
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install:
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- if [ "$TRAVIS_PYTHON_VERSION" == "3.2" ]; then travis_retry pip install coverage==3.7.1; fi
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- if [ "$TRAVIS_PYTHON_VERSION" != "3.2" ]; then travis_retry pip install coverage; fi
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- "pip install pytest pytest-cov"
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script: py.test --doctest-modules --cov ./
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@ -8,7 +8,7 @@ class Node:
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def __init__(self, label):
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self.label = label
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self.left = None
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self.rigt = None
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self.right = None
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def getLabel(self):
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return self.label
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@ -23,10 +23,10 @@ class Node:
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self.left = left
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def getRight(self):
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return self.rigt
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return self.right
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def setRight(self, right):
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self.rigt = right
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self.right = right
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class BinarySearchTree:
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@ -1,9 +1,9 @@
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class GRAPH:
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"""docstring for GRAPH"""
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def __init__(self, nodes):
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self.nodes=nodes
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self.graph=[[0]*nodes for i in range (nodes)]
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self.visited=[0]*nodes
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self.nodes = nodes
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self.graph = [[0]*nodes for i in range (nodes)]
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self.visited = [0]*nodes
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def show(self):
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@ -23,7 +23,7 @@ class GRAPH:
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v = queue[0]
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for u in range(self.vertex):
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if self.graph[v][u] == 1:
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if visited[u]== False:
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if visited[u] is False:
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visited[u] = True
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queue.append(u)
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print('%d visited' % (u +1))
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@ -41,30 +41,32 @@ g.add_edge(4,8)
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g.add_edge(5,9)
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g.add_edge(6,10)
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g.bfs(4)
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=======
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print self.graph
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print(self.graph)
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def add_edge(self, i, j):
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self.graph[i][j]=1
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self.graph[j][i]=1
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def bfs(self,s):
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queue=[s]
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self.visited[s]=1
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while len(queue)!=0:
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x=queue.pop(0)
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def bfs(self, s):
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queue = [s]
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self.visited[s] = 1
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while len(queue)!= 0:
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x = queue.pop(0)
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print(x)
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for i in range(0,self.nodes):
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if self.graph[x][i]==1 and self.visited[i]==0:
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for i in range(0, self.nodes):
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if self.graph[x][i] == 1 and self.visited[i] == 0:
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queue.append(i)
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self.visited[i]=1
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self.visited[i] = 1
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n=int(input("Enter the number of Nodes : "))
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g=GRAPH(n)
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e=int(input("Enter the no of edges : "))
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n = int(input("Enter the number of Nodes : "))
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g = GRAPH(n)
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e = int(input("Enter the no of edges : "))
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print("Enter the edges (u v)")
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for i in range(0,e):
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u,v=map(int, raw_input().split())
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g.add_edge(u,v)
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s=int(input("Enter the source node :"))
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for i in range(0, e):
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u ,v = map(int, raw_input().split())
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g.add_edge(u, v)
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s = int(input("Enter the source node :"))
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g.bfs(s)
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@ -1,61 +0,0 @@
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# Author: OMKAR PATHAK
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class Graph():
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def __init__(self):
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self.vertex = {}
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# for printing the Graph vertexes
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def printGraph(self):
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for i in self.vertex.keys():
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print(i,' -> ', ' -> '.join([str(j) for j in self.vertex[i]]))
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# for adding the edge beween two vertexes
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def addEdge(self, fromVertex, toVertex):
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# check if vertex is already present,
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if fromVertex in self.vertex.keys():
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self.vertex[fromVertex].append(toVertex)
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else:
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# else make a new vertex
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self.vertex[fromVertex] = [toVertex]
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def BFS(self, startVertex):
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# Take a list for stoting already visited vertexes
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visited = [False] * len(self.vertex)
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# create a list to store all the vertexes for BFS
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queue = []
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# mark the source node as visited and enqueue it
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visited[startVertex] = True
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queue.append(startVertex)
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while queue:
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startVertex = queue.pop(0)
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print(startVertex, end = ' ')
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# mark all adjacent nodes as visited and print them
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for i in self.vertex[startVertex]:
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if visited[i] == False:
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queue.append(i)
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visited[i] = True
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if __name__ == '__main__':
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g = Graph()
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g.addEdge(0, 1)
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g.addEdge(0, 2)
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g.addEdge(1, 2)
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g.addEdge(2, 0)
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g.addEdge(2, 3)
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g.addEdge(3, 3)
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g.printGraph()
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print('BFS:')
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g.BFS(2)
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# OUTPUT:
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# 0 -> 1 -> 2
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# 1 -> 2
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# 2 -> 0 -> 3
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# 3 -> 3
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# BFS:
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# 2 0 3 1
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# Author: OMKAR PATHAK
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class Graph():
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def __init__(self):
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self.vertex = {}
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# for printing the Graph vertexes
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def printGraph(self):
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print(self.vertex)
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for i in self.vertex.keys():
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print(i,' -> ', ' -> '.join([str(j) for j in self.vertex[i]]))
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# for adding the edge beween two vertexes
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def addEdge(self, fromVertex, toVertex):
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# check if vertex is already present,
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if fromVertex in self.vertex.keys():
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self.vertex[fromVertex].append(toVertex)
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else:
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# else make a new vertex
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self.vertex[fromVertex] = [toVertex]
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def DFS(self):
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# visited array for storing already visited nodes
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visited = [False] * len(self.vertex)
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# call the recursive helper function
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for i in range(len(self.vertex)):
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if visited[i] == False:
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self.DFSRec(i, visited)
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def DFSRec(self, startVertex, visited):
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# mark start vertex as visited
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visited[startVertex] = True
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print(startVertex, end = ' ')
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# Recur for all the vertexes that are adjacent to this node
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for i in self.vertex.keys():
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if visited[i] == False:
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self.DFSRec(i, visited)
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if __name__ == '__main__':
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g = Graph()
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g.addEdge(0, 1)
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g.addEdge(0, 2)
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g.addEdge(1, 2)
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g.addEdge(2, 0)
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g.addEdge(2, 3)
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g.addEdge(3, 3)
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g.printGraph()
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print('DFS:')
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g.DFS()
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# OUTPUT:
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# 0 -> 1 -> 2
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# 1 -> 2
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# 2 -> 0 -> 3
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# 3 -> 3
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# DFS:
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# 0 1 2 3
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139
machine_learning/decision_tree.py
Normal file
139
machine_learning/decision_tree.py
Normal file
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@ -0,0 +1,139 @@
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"""
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Implementation of a basic regression decision tree.
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Input data set: The input data set must be 1-dimensional with continuous labels.
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Output: The decision tree maps a real number input to a real number output.
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"""
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import numpy as np
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class Decision_Tree:
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def __init__(self, depth = 5, min_leaf_size = 5):
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self.depth = depth
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self.decision_boundary = 0
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self.left = None
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self.right = None
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self.min_leaf_size = min_leaf_size
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self.prediction = None
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def mean_squared_error(self, labels, prediction):
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"""
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mean_squared_error:
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@param labels: a one dimensional numpy array
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@param prediction: a floating point value
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return value: mean_squared_error calculates the error if prediction is used to estimate the labels
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"""
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if labels.ndim != 1:
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print("Error: Input labels must be one dimensional")
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return np.mean((labels - prediction) ** 2)
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def train(self, X, y):
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"""
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train:
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@param X: a one dimensional numpy array
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@param y: a one dimensional numpy array.
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The contents of y are the labels for the corresponding X values
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train does not have a return value
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"""
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"""
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this section is to check that the inputs conform to our dimensionality constraints
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"""
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if X.ndim != 1:
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print("Error: Input data set must be one dimensional")
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return
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if len(X) != len(y):
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print("Error: X and y have different lengths")
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return
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if y.ndim != 1:
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print("Error: Data set labels must be one dimensional")
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return
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if len(X) < 2 * self.min_leaf_size:
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self.prediction = np.mean(y)
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return
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if self.depth == 1:
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self.prediction = np.mean(y)
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return
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best_split = 0
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min_error = self.mean_squared_error(X,np.mean(y)) * 2
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"""
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loop over all possible splits for the decision tree. find the best split.
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if no split exists that is less than 2 * error for the entire array
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then the data set is not split and the average for the entire array is used as the predictor
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"""
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for i in range(len(X)):
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if len(X[:i]) < self.min_leaf_size:
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continue
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elif len(X[i:]) < self.min_leaf_size:
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continue
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else:
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error_left = self.mean_squared_error(X[:i], np.mean(y[:i]))
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error_right = self.mean_squared_error(X[i:], np.mean(y[i:]))
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error = error_left + error_right
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if error < min_error:
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best_split = i
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min_error = error
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if best_split != 0:
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left_X = X[:best_split]
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left_y = y[:best_split]
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right_X = X[best_split:]
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right_y = y[best_split:]
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self.decision_boundary = X[best_split]
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self.left = Decision_Tree(depth = self.depth - 1, min_leaf_size = self.min_leaf_size)
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self.right = Decision_Tree(depth = self.depth - 1, min_leaf_size = self.min_leaf_size)
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self.left.train(left_X, left_y)
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self.right.train(right_X, right_y)
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else:
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self.prediction = np.mean(y)
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return
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def predict(self, x):
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"""
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predict:
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@param x: a floating point value to predict the label of
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the prediction function works by recursively calling the predict function
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of the appropriate subtrees based on the tree's decision boundary
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"""
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if self.prediction is not None:
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return self.prediction
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elif self.left or self.right is not None:
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if x >= self.decision_boundary:
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return self.right.predict(x)
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else:
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return self.left.predict(x)
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else:
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print("Error: Decision tree not yet trained")
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return None
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def main():
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"""
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In this demonstration we're generating a sample data set from the sin function in numpy.
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We then train a decision tree on the data set and use the decision tree to predict the
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label of 10 different test values. Then the mean squared error over this test is displayed.
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"""
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X = np.arange(-1., 1., 0.005)
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y = np.sin(X)
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tree = Decision_Tree(depth = 10, min_leaf_size = 10)
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tree.train(X,y)
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test_cases = (np.random.rand(10) * 2) - 1
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predictions = np.array([tree.predict(x) for x in test_cases])
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avg_error = np.mean((predictions - test_cases) ** 2)
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print("Test values: " + str(test_cases))
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print("Predictions: " + str(predictions))
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print("Average error: " + str(avg_error))
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if __name__ == '__main__':
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main()
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@ -110,9 +110,9 @@ def binary_search_by_recursion(sorted_collection, item, left, right):
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if sorted_collection[midpoint] == item:
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return midpoint
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elif sorted_collection[midpoint] > item:
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return binary_search_by_recursion(sorted_collection, item, left, right-1)
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return binary_search_by_recursion(sorted_collection, item, left, midpoint-1)
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else:
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return binary_search_by_recursion(sorted_collection, item, left+1, right)
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return binary_search_by_recursion(sorted_collection, item, midpoint+1, right)
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def __assert_sorted(collection):
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"""Check if collection is sorted, if not - raises :py:class:`ValueError`
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|
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47
searches/quick_select.py
Normal file
47
searches/quick_select.py
Normal file
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@ -0,0 +1,47 @@
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import collections
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import sys
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import random
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import time
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import math
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"""
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A python implementation of the quick select algorithm, which is efficient for calculating the value that would appear in the index of a list if it would be sorted, even if it is not already sorted
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https://en.wikipedia.org/wiki/Quickselect
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"""
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def _partition(data, pivot):
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"""
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Three way partition the data into smaller, equal and greater lists,
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in relationship to the pivot
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:param data: The data to be sorted (a list)
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:param pivot: The value to partition the data on
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:return: Three list: smaller, equal and greater
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"""
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less, equal, greater = [], [], []
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for element in data:
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if element.address < pivot.address:
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less.append(element)
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elif element.address > pivot.address:
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greater.append(element)
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else:
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equal.append(element)
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return less, equal, greater
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def quickSelect(list, k):
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#k = len(list) // 2 when trying to find the median (index that value would be when list is sorted)
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smaller = []
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larger = []
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pivot = random.randint(0, len(list) - 1)
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pivot = list[pivot]
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count = 0
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smaller, equal, larger =_partition(list, pivot)
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count = len(equal)
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m = len(smaller)
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||||
#k is the pivot
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if m <= k < m + count:
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return pivot
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# must be in smaller
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elif m > k:
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return quickSelect(smaller, k)
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#must be in larger
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else:
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return quickSelect(larger, k - (m + count))
|
||||
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Reference in New Issue
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