Why my code solve word error rate by breadth first search is doesn’t work? - python

This is my code about breadth first search to calculate word error rate[enter image description here](https://i.stack.imgur.com/SV1ow.png)
class FIFOQueue :
def __init__(self) -> None:
self.__queue = []
def append(self,item_in) :
self.__queue.append(item_in)
def extend(self,old_queue):
self.__queue.extend(old_queue)
def pop(self):
return self.__queue.pop(0) # It will be return node
def is_empty(self) :
return self.__queue ==[]
class TreeNode:
def __init__(self,state, step = 0 ,parent=None) -> None:
self.state = tuple(state)
self.step = step
self.parent = parent
def get_state(self):
return self.state
def get_parent(self) :
return self.parent
class RNode(TreeNode) :
def __init__(self, state, step = 0, parent=None) -> None:
super().__init__(state, step, parent)
def expand(self,rp) :
child_list = []
for new_state in rp.adjacent_states(self.state, self.step) :
child_list.append(RNode(new_state, self.step+1, self))
for i in child_list :
print(i.get_state())
return child_list
class RoutingProb : #change
def __init__(self,initial,destination) -> None:
self.initial = initial
self.destination = destination
des = ''
for i in destination :
des += i
self.des_str = des
def is_destination(self,state) :
check_state = ''
for i in state :
if i is None :
continue
else :
check_state += i
return self.des_str == check_state
def adjacent_states(self,state, step) :
return RoutingProb.add_action(self, list(state), step, self.destination)
def add_action(self, state_li, step, des) :
print(step)
print(state_li)
if state_li[step] == des[step]:
print('pass')
print(state_li)
return [state_li]
if len(state_li) > len(des) : #del or sub
state_li2 = state_li[:]
state_li[step] = None #del
state_li2[step] = des[step] #sub
# print('pass2')
return [state_li, state_li2]
elif len(state_li) < len(des) : # ins or sub
state_li2 = state_li[:]
state_li.insert(step, des[step]) #ins
state_li2[step] = des[step] #sub
# print('pass3')
return [state_li, state_li2]
else : # len(state_li) = len(des) only sub
#print('pass4')
state_li[step] = des[step]
return [state_li]
def breadth_first_search(prob):
fringe = FIFOQueue()
fringe.append(RNode(prob.initial))
reaeched ={}
while not fringe.is_empty():
node = fringe.pop()
# print(node.state)
if prob.is_destination(node.state) :
print('xx')
return node
if node.state not in reaeched :
reaeched[node.state] = node
print(reaeched)
fringe.extend(node.expand(prob))
print('x')
def P5_wer(ref,test):
ref_list = [i for i in ref]
test_list = [i for i in test]
rPorb = RoutingProb(test_list,ref_list)
leave_node = breadth_first_search(rPorb)
# print('leave=',leave_node)
x, sol_path = leave_node, [leave_node]
# print('x=',x.get_state)
while x.get_parent() is not None :
sol_path.append(x.get_parent())
x = x.get_parent()
if __name__ == '__main__':
wer, n = P5_wer("grit", "greet")
print("wer = {}, n = {}".format(wer, n))
This is my homework about searching in Introduction to machine learning but it worst course because
they didn't teach me about basic like data structure and Object Oriented Programming. I try my best
I don’t know why it not work

Related

How can I solve Attribute Error in Binary Search Tree Insertion?

I'm studying BST and have reached a problem within this topic.
This is all of the code.
class Node:
# elem is element of Node.
def __init__(self, key, left=None, right=None, parent=None, elem=None):
self.left = left
self.right = right
self.parent = parent
self.key = key
self.elem = elem
# External Node Check
def isExternal(node):
if (node.left is None) and (node.right is None):
return True
else:
return False
# Internal Node Check
def isInternal(node):
if (node.left is not None) and (node.right is not None):
return True
else:
return False
def expandExternal(z):
leftnode = Node()
rightnode = Node()
leftnode.left = None
leftnode.right = None
leftnode.parent = z
rightnode.left = None
rightnode.right = None
rightnode.parent = z
z.left = leftnode
z.right = rightnode
return
class BST:
def __init__(self):
self.root = None
def insertItem(self, key, elem):
node = self.treeSearch(self.root, key)
if Node.isInternal(node):
return
node.elem = elem
node.key = key
Node.expandExternal(node)
return
def treeSearch(self, node, key):
if Node.isExternal(node):
return node
if key == node.key:
return node
elif key < node.key:
return self.treeSearch(node.left, key)
else:
return self.treeSearch(node.right, key)
def main():
n=int(input("ENTER THE NUMBER OF STUDENTS: \n"))
b=BST()
for i in range (n):
print("\nENTER THE DETAILS OF STUDENT ",i+1,":")
key=int(input("Enter the roll num: "))
name=input("Enter name: ")
b.insertItem(key, name)
if __name__ == '__main__':
main()
then Error is
AttributeError: 'NoneType' object has no attribute 'left'
The error occurs in this part:
def isExternal(node):
if (node.left == None) & (node.right == None): # <-- error here
return True
else:
return False
What is wrong with this code?
I wanted to put the roll number as the key and the name as the element and insert them, however I couldn't.

How to find all path of a graph

Update
Thanks to the comments of some community members, I realize that there are some similar problems, but they may a bit different, please allow me to explain it further.
I actually hope to use the same method in a real problem, So briefly:
Reuse of edges in differernt path is completely allowed
a unique(or a new) path from A to B is defined as a collection of vertices that have any different vertices.
Let me use a quiz from Python data structure and algorithm analysis by Bradley .N Miller and David L. Ranum to expain my qusetion.
Quesion:
Consider the task of converting the word FOOL to SAGE, also called word ladder problem. In solving
In the word ladder problem, only one letter must be replaced at a time, and the result of each step must be a word, not non-existent.
Input:
FOUL
FOOL
FOIL
FAIL
COOL
FALL
POOL
PALL
POLL
POLE
PALE
PAGE
SALE
POPE
POPE
SAGE
We can easily find the path from FOOL to SAGE, as Bradley showed:
enter image description here
and I used Breadth First Search (BFS) to solve probem:
class Vertex:
def __init__(self, key, value = None):
self.id = key
self.connectedTo = {}
self.color = 'white'
self.dist = sys.maxsize
self.pred = []
self.disc = 0
self.fin = 0
self.value = value,
#self.GraphBulided = False
self.traverseIndex = 0
self.predNum = 0
def addNeighbor(self, nbr, weight=0):
self.connectedTo[nbr] = weight
def __str__(self):
return '{} connectedTo: {}'.format(self.id, \
str([x.id for x in self.connectedTo]))
def setColor(self, color):
self.color = color
def setDistance(self, d):
self.dist = d
#I want store all Pred for next traverse so I use a list to do it
def setPred(self, p, list = False):
if not list:
self.pred = p
else:
self.pred.append(p)
self.predNum += 1
def setDiscovery(self,dtime):
self.disc = dtime
def setFinish(self,ftime):
self.fin = ftime
#def setGraphBulided(self, tag = True):
# self.GraphBulided = tag
def getFinish(self):
return self.fin
def getDiscovery(self):
return self.disc
def getPred(self):
if isinstance(self.pred, list):
if self.traverseIndex < self.predNum:
return self.pred[self.traverseIndex]
else:
return self.pred[-1]
else:
return self.pred
def __hash__(self):
return hash(self.id)
def getPredById(self):
if self.traverseIndex < self.predNum and isinstance(self.pred, list):
pred = self.pred[self.traverseIndex]
self.traverseIndex += 1
print("vertix {}: {} of {} preds".format(self.id, self.traverseIndex, self.predNum))
return [pred, self.traverseIndex]
else:
pred = None
return [pred, None]
def getCurrPredStaus(self):
#if not self.pred:
# return None
return self.predNum - self.traverseIndex
def getDistance(self):
return self.dist
def getColor(self):
return self.color
def getConnections(self):
return self.connectedTo.keys()
def getId(self):
return self.id
def getWeight(self, nbr):
return self.connectedTo[nbr]
def getValue(self):
return self.value
def findPath(self, dest):
pass
class Graph:
def __init__(self):
self.vertList = {}
self.numVertics = 0
self.verticsInSerach = set()
self.GraphBulided = False
def addVertex(self, key, value = None):
self.numVertics = self.numVertics + 1
newVertex = Vertex(key, value=value)
self.vertList[key] = newVertex
return newVertex
def getVertex(self, n):
if n in self.vertList:
return self.vertList[n]
else:
return None
def __contains__(self, n):
return n in self.vertList
def addEdge(self, f, t, cost = 0, fvalue = None, tvalue = None):
if f not in self.vertList:
nv = self.addVertex(f, fvalue)
if t not in self.vertList:
nv = self.addVertex(t, tvalue)
self.vertList[f].addNeighbor(self.vertList[t], cost)
def setGraphBulided(self, tag = True):
self.GraphBulided = tag
def getVertices(self):
return self.vertList.keys()
def setGraphBulided(self, tag = True):
self.GraphBulided = tag
def setSerachedVertixs(self, vertix):
self.verticsInSerach.add(vertix)
def getGraphBulided(self):
return self.GraphBulided
def getSerachedVertixs(self):
return self.verticsInSerach
def __iter__(self):
return iter(self.vertList.values())
def __hash__(self):
hashIds = [x for x in self.getVertices()]
if len(hashIds) > 0 and hashIds[0]:
return hash(', '.join(hashIds))
else:
return None
Here are some additional functions for building graphs
def buildGraph(wordFile, DFSgraph = False):
d = {}
g = Graph()
if DFSgraph:
g = DFSGraph()
wfile = open(wordFile)
for line in wfile:
word = line[:-1]
for i in range(len(word)):
bucket = word[:i] + '_' + word[i+1:]
if bucket in d:
d[bucket].append(word)
else:
d[bucket] = [word]
for bucket in d.keys():
for word1 in d[bucket]:
for word2 in d[bucket]:
if word1 != word2:
g.addEdge(word1, word2)
wfile.close()
return g
class Queue:
def __init__(self):
self.items = []
def isEmpty(self):
return self.items == []
def enqueue(self, item):
self.items.insert(0,item)
def dequeue(self):
return self.items.pop()
def size(self):
return len(self.items)
def bfs(g, start, listpred = False):
start.setDistance(0)
start.setPred(None)
vertQueue = Queue()
vertQueue.enqueue(start)
while (vertQueue.size() > 0):
currentVert = vertQueue.dequeue()
if currentVert.getConnections():
g.setSerachedVertixs(currentVert)
for nbr in currentVert.getConnections():
#print('sreach {}'.format(currentVert.getId()))
if (nbr.getColor() == 'white' or nbr.getColor() == 'gray'):
nbr.setColor('gray')
nbr.setDistance(currentVert.getDistance() + 1)
if nbr.predNum > 0 and currentVert.getId() not in [x.getId() for x in nbr.pred]:
nbr.setPred(currentVert, listpred)
elif nbr.predNum == 0:
nbr.setPred(currentVert, listpred)
vertQueue.enqueue(nbr)
currentVert.setColor('black')
Therefore, we can easily find the shortest path we need (If we only store one pred for one vertix).
wordGraph = buildGraph('fourletterwords1.txt', DFSgraph=False)
bfs(wordGraph, wordGraph.getVertex('FOOL'), listpred=True)
def traverse(y):
x=y
while(x.getPred()):
print(x.getPred())
x = x.getPred()
print(x.getId())
traverse(wordGraph.getVertex('SAGE'))
However, I still don't know how to trace all the paths correctly, can you give me some suggestions?

FIND path from src to dst ( Dijkstra algorithm )
ADD path to list of paths
LOOP P over list of paths
LOOP V over vertices in P
IF V == src OR V == dst
CONTINUE to next V
COPY graph to working graph
REMOVE V from working graph
FIND path from src to dst in working graph( Dijkstra algorithm )
IF path found
IF path not in list of paths
ADD path to list of paths

Manipulate self object in class

i implement a linked list in Python for my student and particulary a simply version of the "reverse" method ...
This a version that works :
class SimpleList:
def __init__(self, *args):
if len(args) == 0:
self.__cell = None
elif len(args) == 2:
if isinstance(args[1], SimpleList):
self.__cell = (args[0], args[1])
else:
print("Erreur 2")
else:
print("Erreur 3")
def car(self):
if not self.isNull():
return self.__cell[0]
print("Erreur 4")
def cdr(self):
if not self.isNull():
return self.__cell[1]
print("Erreur 5")
def isNull(self):
return self.__cell == None
def __repr__(self):
if self.isNull():
return '()'
else:
return '(' + repr(self.car()) + ',' + repr(self.cdr()) + ')'
def nbrElement(self):
num = 0
tab = self
while not(tab.isNull()):
num = num+1
tab = tab.cdr()
return num
def reverse(self):
num=self.nbrElement()-1
pCourant = SimpleList(self.car(),SimpleList())
tab = self.cdr()
for i in range(0,num-1):
tabTmp = tab
tab = tab.cdr()
tabTmp.__cell = (tabTmp.car(),pCourant)
pCourant= tabTmp
self.__cell = neww = (tab.car(),pCourant)
and the code to execute :
slA = SimpleList(10,SimpleList(9,SimpleList(8,SimpleList(7,SimpleList(6,SimpleList(5,SimpleList(4,SimpleList())))))))
print(slA)
slA.reverse()
print(slA)
but i need to redefine the new tail object
I try to do it only with "swap" the inner link :
def reverse(self):
num=self.nbrElement()-1
pCourant = SimpleList()
tab = self
for i in range(0,num-1):
tabTmp = tab
tab = tab.cdr()
tabTmp.__cell = (tabTmp.car(),pCourant)
pCourant= tabTmp
self.__cell = neww = (tab.car(),pCourant)
There is a personn that can explain me this ???
Thanks for your help
Update :
def reverse(self):
num=self.nbrElement()-1
pCourant = SimpleList()
tab = self
for i in range(0,num):
tabTmp = tab
tab = tab.cdr()
tabTmp.__cell = (tabTmp.car(),pCourant)
pCourant= tabTmp
neww = SimpleList(tab.car(),pCourant)
print(neww)
print(neww)
self.__cell = neww.__cell
slA = SimpleList(10,SimpleList(9,SimpleList(8,SimpleList())))
slA.reverse()
print(slA.cdr().cdr().car())
print(slA)
Is notfor optimize my code ... But for understand, the "inner logic" of Python :
print(new) works well
a print(slA) do a "maximum recursion depth"
the penultimate display offer 8 but normally would display 10
Thanks for all

Since I'm still in the process of drinking my first cup of coffee, here's a simpler reimplementation.
(The __slots__ thing is a pre-optimization; you can elide it if you like.)
class ListCell:
__slots__ = ("value", "next")
def __init__(self, value, next=None):
self.value = value
self.next = next
def __repr__(self):
if not self.next:
return f"({self.value!r})"
else:
return f"({self.value!r},{self.next!r})"
def reverse(self):
prev = next = None
curr = self
while curr:
next = curr.next
curr.next = prev
prev = curr
curr = next
return prev # Return new head
#classmethod
def from_iterable(cls, items):
head = tail = None
for item in items:
if head is None:
head = tail = cls(item)
else:
tail.next = cls(item)
tail = tail.next
return head
lc = ListCell.from_iterable([1, 2, 4, 8])
print(lc)
lc = lc.reverse()
print(lc)
This prints out
(1,(2,(4,(8))))
(8,(4,(2,(1))))

Recursively Creating a Binary Tree Given a String Expression in Prefix Notation in Python

Representation of LinkedBinaryTree that should be outputted
import LinkedBinaryTree
def create_expression_tree(prefix_exp_str):
def create_expression_tree_helper(prefix_exp, start_pos):
start_pos += 1
op = ['+', '-', '*', '/']
elem = prefix_exp[start_pos]
if elem == ' ':
elem = prefix_exp[start_pos+1]
start_pos += 1
if elem not in op:
return LinkedBinaryTree.LinkedBinaryTree.Node(int(elem))
else:
left = create_expression_tree_helper(prefix_exp, start_pos)
right = create_expression_tree_helper(prefix_exp, start_pos+2)
return LinkedBinaryTree.LinkedBinaryTree.Node(elem, left, right)
tree = LinkedBinaryTree.LinkedBinaryTree(create_expression_tree_helper(prefix_exp_str, -1))
return tree
tree1 = create_expression_tree('* 2 + - 15 6 4')
for i in tree1.preorder():
print(i.data, end='')
I implemented my own binary tree class which is shown below. Preorder() is a generator for my LinkedBinaryTree that gives the values of the tree in prefix order. With this code, I'm outputting
*2+-151
but it should be outputting
*2+-1564 if the binary expression tree has been created correctly.
I'm aware that there is an issue with numbers greater than 1 digit, but I'm not sure how to fix it without compromising the runtime (ie. using slicing). Also I'm not sure why it is omitting some of the tokens. Any ideas? (The implementation must run in linear time and use no additional parameters in the helper function I've defined).
import ArrayQueue
class LinkedBinaryTree:
class Node:
def __init__(self, data, left=None, right=None):
self.data = data
self.parent = None
self.left = left
if (self.left is not None):
self.left.parent = self
self.right = right
if (self.right is not None):
self.right.parent = self
def __init__(self, root=None):
self.root = root
self.size = self.subtree_count(root)
def __len__(self):
return self.size
def is_empty(self):
return len(self) == 0
def subtree_count(self, root):
if (root is None):
return 0
else:
left_count = self.subtree_count(root.left)
right_count = self.subtree_count(root.right)
return 1 + left_count + right_count
def sum(self):
return self.subtree_sum(self.root)
def subtree_sum(self, root):
if (root is None):
return 0
else:
left_sum = self.subtree_sum(root.left)
right_sum = self.subtree_sum(root.right)
return root.data + left_sum + right_sum
def height(self):
return self.subtree_height(self.root)
def subtree_height(self, root):
if (root.left is None and root.right is None):
return 0
elif (root.left is None):
return 1 + self.subtree_height(root.right)
elif (root.right is None):
return 1 + self.subtree_height(root.left)
else:
left_height = self.subtree_height(root.left)
right_height = self.subtree_height(root.right)
return 1 + max(left_height, right_height)
def preorder(self):
yield from self.subtree_preorder(self.root)
def subtree_preorder(self, root):
if(root is None):
return
else:
yield root
yield from self.subtree_preorder(root.left)
yield from self.subtree_preorder(root.right)
def postorder(self):
yield from self.subtree_postorder(self.root)
def subtree_postorder(self, root):
if(root is None):
return
else:
yield from self.subtree_postorder(root.left)
yield from self.subtree_postorder(root.right)
yield root
def inorder(self):
yield from self.subtree_inorder(self.root)
def subtree_inorder(self, root):
if(root is None):
return
else:
yield from self.subtree_inorder(root.left)
yield root
yield from self.subtree_inorder(root.right)
def breadth_first(self):
if (self.is_empty()):
return
line = ArrayQueue.ArrayQueue()
line.enqueue(self.root)
while (line.is_empty() == False):
curr_node = line.dequeue()
yield curr_node
if (curr_node.left is not None):
line.enqueue(curr_node.left)
if (curr_node.right is not None):
line.enqueue(curr_node.right)
def __iter__(self):
for node in self.breadth_first():
yield node.data
I added the code here for LinkedBinaryTree class. The ArrayQueue class that is used in the implementation of the breadth traversal method is just a basic queue using a Python list as the underlying data structure.

The two issues with your code were:
you processed the characters one by one while several-digit numbers could be present (fixed by splitting the expression to a list)
you did not account for the fact that chained operator expressions could be longer that just your standard increment (fixed by adding a size property to Node
So here is the new Node class
class Node:
def __init__(self, data, left=None, right=None):
self.data = data
self.parent = None
self.left = left
if (self.left is not None):
self.left.parent = self
self.right = right
if (self.right is not None):
self.right.parent = self
#property
def size(self):
l = 1
if self.left is not None:
l += self.left.size
if self.right is not None:
l += self.right.size
return l
and here is the new tree creator
def create_expression_tree(prefix_exp_str):
expr_lst = prefix_exp_str.split(" ")
op = {'+': None, '-': None, '*': None, '/': None}
def create_expression_tree_helper(prefix_exp, start_pos):
start_pos += 1
elem = prefix_exp[start_pos]
node = None
if elem not in op:
node = LinkedBinaryTree.Node(int(elem))
else:
left = create_expression_tree_helper(prefix_exp, start_pos)
incr = left.size
right = create_expression_tree_helper(prefix_exp, start_pos+incr)
node = LinkedBinaryTree.Node(elem, left, right)
return node
tree = LinkedBinaryTree(create_expression_tree_helper(expr_lst, -1))
return tree
EDIT here is a version that does not require to modify the Node class
def create_expression_tree(prefix_exp_str):
expr_lst = prefix_exp_str.split(" ")
op = {'+': None, '-': None, '*': None, '/': None}
def create_expression_tree_helper(prefix_exp, start_pos):
start_pos += 1
elem = prefix_exp[start_pos]
node = None
size = 1
if elem not in op:
node = LinkedBinaryTree.Node(int(elem))
else:
left, left_size = create_expression_tree_helper(prefix_exp, start_pos)
right, right_size = create_expression_tree_helper(prefix_exp, start_pos+left_size)
node = LinkedBinaryTree.Node(elem, left, right)
size += left_size + right_size
return node, size
tree = LinkedBinaryTree(create_expression_tree_helper(expr_lst, -1)[0])
return tree

How to implement a binary search tree in Python?

This is what I've got so far but it is not working:
class Node:
rChild,lChild,data = None,None,None
def __init__(self,key):
self.rChild = None
self.lChild = None
self.data = key
class Tree:
root,size = None,0
def __init__(self):
self.root = None
self.size = 0
def insert(self,node,someNumber):
if node is None:
node = Node(someNumber)
else:
if node.data > someNumber:
self.insert(node.rchild,someNumber)
else:
self.insert(node.rchild, someNumber)
return
def main():
t = Tree()
t.root = Node(4)
t.root.rchild = Node(5)
print t.root.data #this works
print t.root.rchild.data #this works too
t = Tree()
t.insert(t.root,4)
t.insert(t.root,5)
print t.root.data #this fails
print t.root.rchild.data #this fails too
if __name__ == '__main__':
main()

Here is a quick example of a binary insert:
class Node:
def __init__(self, val):
self.l_child = None
self.r_child = None
self.data = val
def binary_insert(root, node):
if root is None:
root = node
else:
if root.data > node.data:
if root.l_child is None:
root.l_child = node
else:
binary_insert(root.l_child, node)
else:
if root.r_child is None:
root.r_child = node
else:
binary_insert(root.r_child, node)
def in_order_print(root):
if not root:
return
in_order_print(root.l_child)
print root.data
in_order_print(root.r_child)
def pre_order_print(root):
if not root:
return
print root.data
pre_order_print(root.l_child)
pre_order_print(root.r_child)
r = Node(3)
binary_insert(r, Node(7))
binary_insert(r, Node(1))
binary_insert(r, Node(5))
3
/ \
1 7
/
5
print "in order:"
in_order_print(r)
print "pre order"
pre_order_print(r)
in order:
1
3
5
7
pre order
3
1
7
5

class Node:
rChild,lChild,data = None,None,None
This is wrong - it makes your variables class variables - that is, every instance of Node uses the same values (changing rChild of any node changes it for all nodes!). This is clearly not what you want; try
class Node:
def __init__(self, key):
self.rChild = None
self.lChild = None
self.data = key
now each node has its own set of variables. The same applies to your definition of Tree,
class Tree:
root,size = None,0 # <- lose this line!
def __init__(self):
self.root = None
self.size = 0
Further, each class should be a "new-style" class derived from the "object" class and should chain back to object.__init__():
class Node(object):
def __init__(self, data, rChild=None, lChild=None):
super(Node,self).__init__()
self.data = data
self.rChild = rChild
self.lChild = lChild
class Tree(object):
def __init__(self):
super(Tree,self).__init__()
self.root = None
self.size = 0
Also, main() is indented too far - as shown, it is a method of Tree which is uncallable because it does not accept a self argument.
Also, you are modifying the object's data directly (t.root = Node(4)) which kind of destroys encapsulation (the whole point of having classes in the first place); you should be doing something more like
def main():
t = Tree()
t.add(4) # <- let the tree create a data Node and insert it
t.add(5)

class Node:
rChild,lChild,parent,data = None,None,None,0
def __init__(self,key):
self.rChild = None
self.lChild = None
self.parent = None
self.data = key
class Tree:
root,size = None,0
def __init__(self):
self.root = None
self.size = 0
def insert(self,someNumber):
self.size = self.size+1
if self.root is None:
self.root = Node(someNumber)
else:
self.insertWithNode(self.root, someNumber)
def insertWithNode(self,node,someNumber):
if node.lChild is None and node.rChild is None:#external node
if someNumber > node.data:
newNode = Node(someNumber)
node.rChild = newNode
newNode.parent = node
else:
newNode = Node(someNumber)
node.lChild = newNode
newNode.parent = node
else: #not external
if someNumber > node.data:
if node.rChild is not None:
self.insertWithNode(node.rChild, someNumber)
else: #if empty node
newNode = Node(someNumber)
node.rChild = newNode
newNode.parent = node
else:
if node.lChild is not None:
self.insertWithNode(node.lChild, someNumber)
else:
newNode = Node(someNumber)
node.lChild = newNode
newNode.parent = node
def printTree(self,someNode):
if someNode is None:
pass
else:
self.printTree(someNode.lChild)
print someNode.data
self.printTree(someNode.rChild)
def main():
t = Tree()
t.insert(5)
t.insert(3)
t.insert(7)
t.insert(4)
t.insert(2)
t.insert(1)
t.insert(6)
t.printTree(t.root)
if __name__ == '__main__':
main()
My solution.

class BST:
def __init__(self, val=None):
self.left = None
self.right = None
self.val = val
def __str__(self):
return "[%s, %s, %s]" % (self.left, str(self.val), self.right)
def isEmpty(self):
return self.left == self.right == self.val == None
def insert(self, val):
if self.isEmpty():
self.val = val
elif val < self.val:
if self.left is None:
self.left = BST(val)
else:
self.left.insert(val)
else:
if self.right is None:
self.right = BST(val)
else:
self.right.insert(val)
a = BST(1)
a.insert(2)
a.insert(3)
a.insert(0)
print a

The Op's Tree.insert method qualifies for the "Gross Misnomer of the Week" award -- it doesn't insert anything. It creates a node which is not attached to any other node (not that there are any nodes to attach it to) and then the created node is trashed when the method returns.
For the edification of #Hugh Bothwell:
>>> class Foo(object):
... bar = None
...
>>> a = Foo()
>>> b = Foo()
>>> a.bar
>>> a.bar = 42
>>> b.bar
>>> b.bar = 666
>>> a.bar
42
>>> b.bar
666
>>>

The accepted answer neglects to set a parent attribute for each node inserted, without which one cannot implement a successor method which finds the successor in an in-order tree walk in O(h) time, where h is the height of the tree (as opposed to the O(n) time needed for the walk).
Here is an implementation based on the pseudocode given in Cormen et al., Introduction to Algorithms, including assignment of a parent attribute and a successor method:
class Node(object):
def __init__(self, key):
self.key = key
self.left = None
self.right = None
self.parent = None
class Tree(object):
def __init__(self, root=None):
self.root = root
def insert(self, z):
y = None
x = self.root
while x is not None:
y = x
if z.key < x.key:
x = x.left
else:
x = x.right
z.parent = y
if y is None:
self.root = z # Tree was empty
elif z.key < y.key:
y.left = z
else:
y.right = z
#staticmethod
def minimum(x):
while x.left is not None:
x = x.left
return x
#staticmethod
def successor(x):
if x.right is not None:
return Tree.minimum(x.right)
y = x.parent
while y is not None and x == y.right:
x = y
y = y.parent
return y
Here are some tests to show that the tree behaves as expected for the example given by DTing:
import pytest
#pytest.fixture
def tree():
t = Tree()
t.insert(Node(3))
t.insert(Node(1))
t.insert(Node(7))
t.insert(Node(5))
return t
def test_tree_insert(tree):
assert tree.root.key == 3
assert tree.root.left.key == 1
assert tree.root.right.key == 7
assert tree.root.right.left.key == 5
def test_tree_successor(tree):
assert Tree.successor(tree.root.left).key == 3
assert Tree.successor(tree.root.right.left).key == 7
if __name__ == "__main__":
pytest.main([__file__])

Just something to help you to start on.
A (simple idea of) binary tree search would be quite likely be implement in python according the lines:
def search(node, key):
if node is None: return None # key not found
if key< node.key: return search(node.left, key)
elif key> node.key: return search(node.right, key)
else: return node.value # found key
Now you just need to implement the scaffolding (tree creation and value inserts) and you are done.

I find the solutions a bit clumsy on the insert part. You could return the root reference and simplify it a bit:
def binary_insert(root, node):
if root is None:
return node
if root.data > node.data:
root.l_child = binary_insert(root.l_child, node)
else:
root.r_child = binary_insert(root.r_child, node)
return root

its easy to implement a BST using two classes, 1. Node and 2. Tree
Tree class will be just for user interface, and actual methods will be implemented in Node class.
class Node():
def __init__(self,val):
self.value = val
self.left = None
self.right = None
def _insert(self,data):
if data == self.value:
return False
elif data < self.value:
if self.left:
return self.left._insert(data)
else:
self.left = Node(data)
return True
else:
if self.right:
return self.right._insert(data)
else:
self.right = Node(data)
return True
def _inorder(self):
if self:
if self.left:
self.left._inorder()
print(self.value)
if self.right:
self.right._inorder()
class Tree():
def __init__(self):
self.root = None
def insert(self,data):
if self.root:
return self.root._insert(data)
else:
self.root = Node(data)
return True
def inorder(self):
if self.root is not None:
return self.root._inorder()
else:
return False
if __name__=="__main__":
a = Tree()
a.insert(16)
a.insert(8)
a.insert(24)
a.insert(6)
a.insert(12)
a.insert(19)
a.insert(29)
a.inorder()
Inorder function for checking whether BST is properly implemented.

Another Python BST with sort key (defaulting to value)
LEFT = 0
RIGHT = 1
VALUE = 2
SORT_KEY = -1
class BinarySearchTree(object):
def __init__(self, sort_key=None):
self._root = []
self._sort_key = sort_key
self._len = 0
def insert(self, val):
if self._sort_key is None:
sort_key = val // if no sort key, sort key is value
else:
sort_key = self._sort_key(val)
node = self._root
while node:
if sort_key < node[_SORT_KEY]:
node = node[LEFT]
else:
node = node[RIGHT]
if sort_key is val:
node[:] = [[], [], val]
else:
node[:] = [[], [], val, sort_key]
self._len += 1
def minimum(self):
return self._extreme_node(LEFT)[VALUE]
def maximum(self):
return self._extreme_node(RIGHT)[VALUE]
def find(self, sort_key):
return self._find(sort_key)[VALUE]
def _extreme_node(self, side):
if not self._root:
raise IndexError('Empty')
node = self._root
while node[side]:
node = node[side]
return node
def _find(self, sort_key):
node = self._root
while node:
node_key = node[SORT_KEY]
if sort_key < node_key:
node = node[LEFT]
elif sort_key > node_key:
node = node[RIGHT]
else:
return node
raise KeyError("%r not found" % sort_key)

Here is a compact, object oriented, recursive implementation:
class BTreeNode(object):
def __init__(self, data):
self.data = data
self.rChild = None
self.lChild = None
def __str__(self):
return (self.lChild.__str__() + '<-' if self.lChild != None else '') + self.data.__str__() + ('->' + self.rChild.__str__() if self.rChild != None else '')
def insert(self, btreeNode):
if self.data > btreeNode.data: #insert left
if self.lChild == None:
self.lChild = btreeNode
else:
self.lChild.insert(btreeNode)
else: #insert right
if self.rChild == None:
self.rChild = btreeNode
else:
self.rChild.insert(btreeNode)
def main():
btreeRoot = BTreeNode(5)
print 'inserted %s:' %5, btreeRoot
btreeRoot.insert(BTreeNode(7))
print 'inserted %s:' %7, btreeRoot
btreeRoot.insert(BTreeNode(3))
print 'inserted %s:' %3, btreeRoot
btreeRoot.insert(BTreeNode(1))
print 'inserted %s:' %1, btreeRoot
btreeRoot.insert(BTreeNode(2))
print 'inserted %s:' %2, btreeRoot
btreeRoot.insert(BTreeNode(4))
print 'inserted %s:' %4, btreeRoot
btreeRoot.insert(BTreeNode(6))
print 'inserted %s:' %6, btreeRoot
The output of the above main() is:
inserted 5: 5
inserted 7: 5->7
inserted 3: 3<-5->7
inserted 1: 1<-3<-5->7
inserted 2: 1->2<-3<-5->7
inserted 4: 1->2<-3->4<-5->7
inserted 6: 1->2<-3->4<-5->6<-7

Here is a working solution.
class BST:
def __init__(self,data):
self.root = data
self.left = None
self.right = None
def insert(self,data):
if self.root == None:
self.root = BST(data)
elif data > self.root:
if self.right == None:
self.right = BST(data)
else:
self.right.insert(data)
elif data < self.root:
if self.left == None:
self.left = BST(data)
else:
self.left.insert(data)
def inordertraversal(self):
if self.left != None:
self.left.inordertraversal()
print (self.root),
if self.right != None:
self.right.inordertraversal()
t = BST(4)
t.insert(1)
t.insert(7)
t.insert(3)
t.insert(6)
t.insert(2)
t.insert(5)
t.inordertraversal()

A simple, recursive method with only 1 function and using an array of values:
class TreeNode(object):
def __init__(self, value: int, left=None, right=None):
super().__init__()
self.value = value
self.left = left
self.right = right
def __str__(self):
return str(self.value)
def create_node(values, lower, upper) -> TreeNode:
if lower > upper:
return None
index = (lower + upper) // 2
value = values[index]
node = TreeNode(value=value)
node.left = create_node(values, lower, index - 1)
node.right = create_node(values, index + 1, upper)
return node
def print_bst(node: TreeNode):
if node:
# Simple pre-order traversal when printing the tree
print("node: {}".format(node))
print_bst(node.left)
print_bst(node.right)
if __name__ == '__main__':
vals = [0, 1, 2, 3, 4, 5, 6]
bst = create_node(vals, lower=0, upper=len(vals) - 1)
print_bst(bst)
As you can see, we really only need 1 method, which is recursive: create_node. We pass in the full values array in each create_node method call, however, we update the lower and upper index values every time that we make the recursive call.
Then, using the lower and upper index values, we calculate the index value of the current node and capture it in value. This value is the value for the current node, which we use to create a node.
From there, we set the values of left and right by recursively calling the function, until we reach the end state of the recursion call when lower is greater than upper.
Important: we update the value of upper when creating the left side of the tree. Conversely, we update the value of lower when creating the right side of the tree.
Hopefully this helps!

The following code is basic on #DTing‘s answer and what I learn from class, which uses a while loop to insert (indicated in the code).
class Node:
def __init__(self, val):
self.l_child = None
self.r_child = None
self.data = val
def binary_insert(root, node):
y = None
x = root
z = node
#while loop here
while x is not None:
y = x
if z.data < x.data:
x = x.l_child
else:
x = x.r_child
z.parent = y
if y == None:
root = z
elif z.data < y.data:
y.l_child = z
else:
y.r_child = z
def in_order_print(root):
if not root:
return
in_order_print(root.l_child)
print(root.data)
in_order_print(root.r_child)
r = Node(3)
binary_insert(r, Node(7))
binary_insert(r, Node(1))
binary_insert(r, Node(5))
in_order_print(r)

The problem, or at least one problem with your code is here:-
def insert(self,node,someNumber):
if node is None:
node = Node(someNumber)
else:
if node.data > someNumber:
self.insert(node.rchild,someNumber)
else:
self.insert(node.rchild, someNumber)
return
You see the statement "if node.data > someNumber:" and the associated "else:" statement both have the same code after them. i.e you do the same thing whether the if statement is true or false.
I'd suggest you probably intended to do different things here, perhaps one of these should say self.insert(node.lchild, someNumber) ?

Another Python BST solution
class Node(object):
def __init__(self, value):
self.left_node = None
self.right_node = None
self.value = value
def __str__(self):
return "[%s, %s, %s]" % (self.left_node, self.value, self.right_node)
def insertValue(self, new_value):
"""
1. if current Node doesnt have value then assign to self
2. new_value lower than current Node's value then go left
2. new_value greater than current Node's value then go right
:return:
"""
if self.value:
if new_value < self.value:
# add to left
if self.left_node is None: # reached start add value to start
self.left_node = Node(new_value)
else:
self.left_node.insertValue(new_value) # search
elif new_value > self.value:
# add to right
if self.right_node is None: # reached end add value to end
self.right_node = Node(new_value)
else:
self.right_node.insertValue(new_value) # search
else:
self.value = new_value
def findValue(self, value_to_find):
"""
1. value_to_find is equal to current Node's value then found
2. if value_to_find is lower than Node's value then go to left
3. if value_to_find is greater than Node's value then go to right
"""
if value_to_find == self.value:
return "Found"
elif value_to_find < self.value and self.left_node:
return self.left_node.findValue(value_to_find)
elif value_to_find > self.value and self.right_node:
return self.right_node.findValue(value_to_find)
return "Not Found"
def printTree(self):
"""
Nodes will be in sequence
1. Print LHS items
2. Print value of node
3. Print RHS items
"""
if self.left_node:
self.left_node.printTree()
print(self.value),
if self.right_node:
self.right_node.printTree()
def isEmpty(self):
return self.left_node == self.right_node == self.value == None
def main():
root_node = Node(12)
root_node.insertValue(6)
root_node.insertValue(3)
root_node.insertValue(7)
# should return 3 6 7 12
root_node.printTree()
# should return found
root_node.findValue(7)
# should return found
root_node.findValue(3)
# should return Not found
root_node.findValue(24)
if __name__ == '__main__':
main()

def BinaryST(list1,key):
start = 0
end = len(list1)
print("Length of List: ",end)
for i in range(end):
for j in range(0, end-i-1):
if(list1[j] > list1[j+1]):
temp = list1[j]
list1[j] = list1[j+1]
list1[j+1] = temp
print("Order List: ",list1)
mid = int((start+end)/2)
print("Mid Index: ",mid)
if(key == list1[mid]):
print(key," is on ",mid," Index")
elif(key > list1[mid]):
for rindex in range(mid+1,end):
if(key == list1[rindex]):
print(key," is on ",rindex," Index")
break
elif(rindex == end-1):
print("Given key: ",key," is not in List")
break
else:
continue
elif(key < list1[mid]):
for lindex in range(0,mid):
if(key == list1[lindex]):
print(key," is on ",lindex," Index")
break
elif(lindex == mid-1):
print("Given key: ",key," is not in List")
break
else:
continue
size = int(input("Enter Size of List: "))
list1 = []
for e in range(size):
ele = int(input("Enter Element in List: "))
list1.append(ele)
key = int(input("\nEnter Key for Search: "))
print("\nUnorder List: ",list1)
BinaryST(list1,key)

class TreeNode:
def __init__(self, value):
self.value = value
self.left = None
self.right = None
class BinaryTree:
def __init__(self, root=None):
self.root = root
def add_node(self, node, value):
"""
Node points to the left of value if node > value; right otherwise,
BST cannot have duplicate values
"""
if node is not None:
if value < node.value:
if node.left is None:
node.left = TreeNode(value)
else:
self.add_node(node.left, value)
else:
if node.right is None:
node.right = TreeNode(value)
else:
self.add_node(node.right, value)
else:
self.root = TreeNode(value)
def search(self, value):
"""
Value will be to the left of node if node > value; right otherwise.
"""
node = self.root
while node is not None:
if node.value == value:
return True # node.value
if node.value > value:
node = node.left
else:
node = node.right
return False
def traverse_inorder(self, node):
"""
Traverse the left subtree of a node as much as possible, then traverse
the right subtree, followed by the parent/root node.
"""
if node is not None:
self.traverse_inorder(node.left)
print(node.value)
self.traverse_inorder(node.right)
def main():
binary_tree = BinaryTree()
binary_tree.add_node(binary_tree.root, 200)
binary_tree.add_node(binary_tree.root, 300)
binary_tree.add_node(binary_tree.root, 100)
binary_tree.add_node(binary_tree.root, 30)
binary_tree.traverse_inorder(binary_tree.root)
print(binary_tree.search(200))
if __name__ == '__main__':
main()

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