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Merge pull request #31 from nancyanand2807/master

BFS
pull/33/head
Jeffery Russell 5 years ago
committed by GitHub
parent
commit
c42c3c9e17
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4 changed files with 203 additions and 0 deletions
  1. +41
    -0
      ML Cookbook/perceptron.py
  2. +41
    -0
      ML Cookbook/perceptron1.py
  3. +63
    -0
      bfs.py
  4. +58
    -0
      dfs.py

+ 41
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ML Cookbook/perceptron.py View File

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import numpy as np
class Perceptron(object):
"""Implements a perceptron network"""
def __init__(self, input_size, lr=1, epochs=100):
self.W = np.zeros(input_size+1)
# add one for bias
self.epochs = epochs
self.lr = lr
#activation function
def activation_fn(self, x):
#return (x >= 0).astype(np.float32)
return 1 if x >= 0 else 0
#we need a prediction function to run an input through the perceptron and return an output.
def predict(self, x):
z = self.W.T.dot(x)
a = self.activation_fn(z)
return a
def fit(self, X, d):
for _ in range(self.epochs):
for i in range(d.shape[0]):
x = np.insert(X[i], 0, 1)
y = self.predict(x)
e = d[i] - y
self.W = self.W + self.lr * e * x
#the easiset set of data that we can provide is the AND gate. Given is set of inputs and outputs.
if __name__ == '__main__':
X = np.array([
[0, 0],
[0, 1],
[1, 0],
[1, 1]
])
d = np.array([0, 0, 0, 1])
perceptron = Perceptron(input_size=2)
perceptron.fit(X, d)
print(perceptron.W)

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ML Cookbook/perceptron1.py View File

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import numpy as np
class Perceptron(object):
"""Implements a perceptron network"""
def __init__(self, input_size, lr=1, epochs=100):
self.W = np.zeros(input_size+1)
# add one for bias
self.epochs = epochs
self.lr = lr
#activation function
def activation_fn(self, x):
#return (x >= 0).astype(np.float32)
return 1 if x >= 0 else 0
#we need a prediction function to run an input through the perceptron and return an output.
def predict(self, x):
z = self.W.T.dot(x)
a = self.activation_fn(z)
return a
def fit(self, X, d):
for _ in range(self.epochs):
for i in range(d.shape[0]):
x = np.insert(X[i], 0, 1)
y = self.predict(x)
e = d[i] - y
self.W = self.W + self.lr * e * x
#the easiset set of data that we can provide is the AND gate. Given is set of inputs and outputs.
if __name__ == '__main__':
X = np.array([
[0, 0],
[0, 1],
[1, 0],
[1, 1]
])
d = np.array([0, 0, 0, 1])
perceptron = Perceptron(input_size=2)
perceptron.fit(X, d)
print(perceptron.W)

+ 63
- 0
bfs.py View File

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# Python3 Program to print BFS traversal
from collections import defaultdict
# This class represents a directed graph
# using adjacency list representation
class Graph:
# Constructor
def __init__(self):
# default dictionary to store graph
self.graph = defaultdict(list)
# function to add an edge to graph
def addEdge(self,u,v):
self.graph[u].append(v)
# Function to print a BFS of graph
def BFS(self, s):
# Mark all the vertices as not visited
visited = [False] * (len(self.graph))
# Create a queue for BFS
queue = []
# Mark the source node as
# visited and enqueue it
queue.append(s)
visited[s] = True
while queue:
# Dequeue a vertex from
# queue and print it
s = queue.pop(0)
print (s, end = " ")
# Get all adjacent vertices of the
# dequeued vertex s. If a adjacent
# has not been visited, then mark it
# visited and enqueue it
for i in self.graph[s]:
if visited[i] == False:
queue.append(i)
visited[i] = True
# Driver code
g = Graph()
g.addEdge(0, 1)
g.addEdge(0, 2)
g.addEdge(1, 2)
g.addEdge(2, 0)
g.addEdge(2, 3)
g.addEdge(3, 3)
print ("Following is Breadth First Traversal"
" (starting from vertex 2)")
g.BFS(2)

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dfs.py View File

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# Python3 program to print DFS traversal
from collections import defaultdict
# This class represents a directed graph using
# adjacency list representation
class Graph:
# Constructor
def __init__(self):
# default dictionary to store graph
self.graph = defaultdict(list)
def addEdge(self, u, v):
self.graph[u].append(v)
# A function used by DFS
def DFSUtil(self, v, visited):
# Mark the current node as visited
# and print it
visited[v] = True
print(v, end = ' ')
# Recur for all the vertices
# adjacent to this vertex
for i in self.graph[v]:
if visited[i] == False:
self.DFSUtil(i, visited)
# The function to do DFS traversal. It uses
# recursive DFSUtil()
def DFS(self, v):
# Mark all the vertices as not visited
visited = [False] * (len(self.graph))
# Call the recursive helper function
# to print DFS traversal
self.DFSUtil(v, visited)
# Driver code
# Create a graph given
# in the above diagram
g = Graph()
g.addEdge(0, 1)
g.addEdge(0, 2)
g.addEdge(1, 2)
g.addEdge(2, 0)
g.addEdge(2, 3)
g.addEdge(3, 3)
print("Following is DFS from (starting from vertex 2)")
g.DFS(2)

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