What are the KBF and the digital value proposition offered by ASOS to.pdf
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ASOS targets fashion-conscious young adults with its online fashion retailer ASOS. It offers a wide selection of on-trend fashion from established and emerging brands through its website and mobile app. ASOS's digital value proposition is providing an engaging and personalized shopping experience through social features, video content, and fast fulfillment to meet the needs of its target demographic.
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What is the complete code for this problem?
Create a KakuroCSP solver using python with the Requirements:
Code (separate functions must be implemented for each one)
CSP formulation
Backtracking search
Forward Checking
AC3 (for the initial check)
MAC (Maintaining Arc Consistency)
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MRV (Minimum Remaining Values)
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FC=false
AC3=false
MAC=false
NC=false
MRV=false,
LCV=false
rows=5
columns=5
#,#,#,#,#
#,0,0,#,#
#,0,0,0,0
#,0,0,0,0
#,#,#,0,0
Constraints (row,column,sum,direction)
1,2,24,v
1,3,21,v
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2,4,23,v
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#,#,#,#,#
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#,9,7,6,8
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# CSP Formulation
def KakuroCSPFormulation(problem):
# Variables
variables = []
for row in range(1, problem['rows']+1):
for column in range(1, problem['columns']+1):
if problem['grid'][row-1][column-1] == 0:
variables.append((row, column))
# Domains
domains = {var: [1, 2, 3, 4, 5, 6, 7, 8, 9] for var in variables}
# Constraints
constraints = []
for constraint in problem['constraints']:
row, column, sum, direction = constraint
if direction == 'h':
for i in range(column, problem['columns']+1):
if problem['grid'][row-1][i-1] == 0:
constraints.append(KakuroConstraint(sum, (row, column), (row, i)))
elif direction == 'v':
for i in range(row, problem['rows']+1):
if problem['grid'][i-1][column-1] == 0:
constraints.append(KakuroConstraint(sum, (row, column), (i, column)))
# Return the CSP
return CSP(variables, domains, constraints)
# Constraint
class KakuroConstraint:
def __init__(self, sum, left_var, right_var):
self.sum = sum
self.left_var = left_var
self.right_var = right_var
def satisfied(self, assignment):
left_val = assignment.get(self.left_var)
right_val = assignment.get(self.right_var)
if left_val is None or right_val is None:
return True
return left_val + right_val == self.sum
# CSP
class CSP:
def __init__(self, variables, domains, constraints):
self.variables = variables
self.domains = domains
self.constraints = constraints
# Backtracking Search with Optional Heuristics
def KakuroBacktrackingSearch(problem, FC=False, AC3=False, MAC=False, NC=False,
MRV=False, LCV=False):
assignment = {}
return KakuroRecursiveBacktracking(problem, assignment, FC, AC3, MAC, NC, MRV, LCV)
# Recursive Backtracking
def KakuroRecursiveBacktracking(problem, assignment, FC, AC3, MAC, NC, MRV, LCV):
# Check if the assignment is complete
if len(assignment) == len(problem.variables):
return assignment
# Select unassigned variable
unassigned = [var for var in problem.variables if var not in assignment]
if MRV:
unassigned = sorted(unassigned, key=lambda var: KakuroMRV(problem, assignment, var))
var = unassigned[.
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DON'T POST A MISSING CODE PLEASE PUT THE EVERYTHING INCLUDING THE
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Create a KakuroCSP solver using python with the Requirements:
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Forward Checking
AC3 (for the initial check)
MAC (Maintaining Arc Consistency)
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MRV (Minimum Remaining Values)
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FC=false
AC3=false
MAC=false
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MRV=false,
LCV=false
rows=5
columns=5
#,#,#,#,#
#,0,0,#,#
#,0,0,0,0
#,0,0,0,0
#,#,#,0,0
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1,2,24,v
1,3,21,v
2,1,17,h
2,4,23,v
2,5,24,v
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4,1,29,h
5,3,16,h
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#,#,#,#,#
#,8,9,#,#
#,9,7,6,8
#,7,5,8,9
#,#,#,9,7
code is not finished and needs the rest of the code.
# CSP Formulation
def KakuroCSPFormulation(problem):
# Variables
variables = []
for row in range(1, problem['rows']+1):
for column in range(1, problem['columns']+1):
if problem['grid'][row-1][column-1] == 0:
variables.append((row, column))
# Domains
domains = {var: [1, 2, 3, 4, 5, 6, 7, 8, 9] for var in variables}
# Constraints
constraints = []
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row, column, sum, direction = constraint
if direction == 'h':
for i in range(column, problem['columns']+1):
if problem['grid'][row-1][i-1] == 0:
constraints.append(KakuroConstraint(sum, (row, column), (row, i)))
elif direction == 'v':
for i in range(row, problem['rows']+1):
if problem['grid'][i-1][column-1] == 0:
constraints.append(KakuroConstraint(sum, (row, column), (i, column)))
# Return the CSP
return CSP(variables, domains, constraints)
# Constraint
class KakuroConstraint:
def __init__(self, sum, left_var, right_var):
self.sum = sum
self.left_var = left_var
self.right_var = right_var
def satisfied(self, assignment):
left_val = assignment.get(self.left_var)
right_val = assignment.get(self.right_var)
if left_val is None or right_val is None:
return True
return left_val + right_val == self.sum
# CSP
class CSP:
def __init__(self, variables, domains, constraints):
self.variables = variables
self.domains = domains
self.constraints = constraints
# Backtracking Search with Optional Heuristics
def KakuroBacktrackingSearch(problem, FC=False, AC3=False, MAC=False, NC=False,
MRV=False, LCV=False):
assignment = {}
return KakuroRecursiveBacktracking(problem, assignment, FC, AC3, MAC, NC, MRV, LCV)
# Recursive Backtracking
def KakuroRecursiveBacktracking(problem, assignment, FC, AC3, MAC, NC, MRV, LCV):
# Check if the assignment is complete
if len(assignment) == len(problem.variables):
return assignment
# Select unassigned variable
unassigned = [var for var in problem.variables if var not in assignment]
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What is the complete code for this problem?
Create a KakuroCSP solver using python with the Requirements:
Code (separate functions must be implemented for each one)
CSP formulation
Backtracking search
Forward Checking
AC3 (for the initial check)
MAC (Maintaining Arc Consistency)
Node consistency
MRV (Minimum Remaining Values)
LCV (Least Constraining Value)
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FC=false
AC3=false
MAC=false
NC=false
MRV=false,
LCV=false
rows=5
columns=5
#,#,#,#,#
#,0,0,#,#
#,0,0,0,0
#,0,0,0,0
#,#,#,0,0
Constraints (row,column,sum,direction)
1,2,24,v
1,3,21,v
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2,4,23,v
2,5,24,v
3,1,30,h
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# CSP Formulation
def KakuroCSPFormulation(problem):
# Variables
variables = []
for row in range(1, problem['rows']+1):
for column in range(1, problem['columns']+1):
if problem['grid'][row-1][column-1] == 0:
variables.append((row, column))
# Domains
domains = {var: [1, 2, 3, 4, 5, 6, 7, 8, 9] for var in variables}
# Constraints
constraints = []
for constraint in problem['constraints']:
row, column, sum, direction = constraint
if direction == 'h':
for i in range(column, problem['columns']+1):
if problem['grid'][row-1][i-1] == 0:
constraints.append(KakuroConstraint(sum, (row, column), (row, i)))
elif direction == 'v':
for i in range(row, problem['rows']+1):
if problem['grid'][i-1][column-1] == 0:
constraints.append(KakuroConstraint(sum, (row, column), (i, column)))
# Return the CSP
return CSP(variables, domains, constraints)
# Constraint
class KakuroConstraint:
def __init__(self, sum, left_var, right_var):
self.sum = sum
self.left_var = left_var
self.right_var = right_var
def satisfied(self, assignment):
left_val = assignment.get(self.left_var)
right_val = assignment.get(self.right_var)
if left_val is None or right_val is None:
return True
return left_val + right_val == self.sum
# CSP
class CSP:
def __init__(self, variables, domains, constraints):
self.variables = variables
self.domains = domains
self.constraints = constraints
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def KakuroBacktrackingSearch(problem, FC=False, AC3=False, MAC=False, NC=False,
MRV=False, LCV=False):
assignment = {}
return KakuroRecursiveBacktracking(problem, assignment, FC, AC3, MAC, NC, MRV, LCV)
# Recursive Backtracking
def KakuroRecursiveBacktracking(problem, assignment, FC, AC3, MAC, NC, MRV, LCV):
# Check if the assignment is complete
if len(assignment) == len(problem.variables):
return assignment
# Select unassigned variable
unassigned = [var for var in problem.variables if var not in assignment]
if MRV:
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var = unassigned[.
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MRV (Minimum Remaining Values)
LCV (Least Constraining Value)
Provide the complete code and explain how to run the code, please.
Input:
The input is the name of a text file in the following format:
FC=false
AC3=false
MAC=false
NC=false
MRV=false,
LCV=false
rows=5
columns=5
#,#,#,#,#
#,0,0,#,#
#,0,0,0,0
#,0,0,0,0
#,#,#,0,0
Constraints (row,column,sum,direction)
1,2,24,v
1,3,21,v
2,1,17,h
2,4,23,v
2,5,24,v
3,1,30,h
4,1,29,h
5,3,16,h
Output:
The output is a text file containing the solved puzzle in the following format (the zeros in the
input are replaced with the numbers):
#,#,#,#,#
#,8,9,#,#
#,9,7,6,8
#,7,5,8,9
#,#,#,9,7
code is not finished and needs the rest of the code.
# CSP Formulation
def KakuroCSPFormulation(problem):
# Variables
variables = []
for row in range(1, problem['rows']+1):
for column in range(1, problem['columns']+1):
if problem['grid'][row-1][column-1] == 0:
variables.append((row, column))
# Domains
domains = {var: [1, 2, 3, 4, 5, 6, 7, 8, 9] for var in variables}
# Constraints
constraints = []
for constraint in problem['constraints']:
row, column, sum, direction = constraint
if direction == 'h':
for i in range(column, problem['columns']+1):
if problem['grid'][row-1][i-1] == 0:
constraints.append(KakuroConstraint(sum, (row, column), (row, i)))
elif direction == 'v':
for i in range(row, problem['rows']+1):
if problem['grid'][i-1][column-1] == 0:
constraints.append(KakuroConstraint(sum, (row, column), (i, column)))
# Return the CSP
return CSP(variables, domains, constraints)
# Constraint
class KakuroConstraint:
def __init__(self, sum, left_var, right_var):
self.sum = sum
self.left_var = left_var
self.right_var = right_var
def satisfied(self, assignment):
left_val = assignment.get(self.left_var)
right_val = assignment.get(self.right_var)
if left_val is None or right_val is None:
return True
return left_val + right_val == self.sum
# CSP
class CSP:
def __init__(self, variables, domains, constraints):
self.variables = variables
self.domains = domains
self.constraints = constraints
# Backtracking Search with Optional Heuristics
def KakuroBacktrackingSearch(problem, FC=False, AC3=False, MAC=False, NC=False,
MRV=False, LCV=False):
assignment = {}
return KakuroRecursiveBacktracking(problem, assignment, FC, AC3, MAC, NC, MRV, LCV)
# Recursive Backtracking
def KakuroRecursiveBacktracking(problem, assignment, FC, AC3, MAC, NC, MRV, LCV):
# Check if the assignment is complete
if len(assignment) == len(problem.variables):
return assignment
# Select unassigned variable
unassigned = [var for var in problem.variables if var not in assignment]
if MRV:
unassigned = sorted(unassigned.
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