def knapsack_simulation(name):
data = db.knapsack.find_one({"_id": ObjectId(name)})
array1 = ast.literal_eval(data['knapsack_array1'])
arr_size1 = 0
array2 = ast.literal_eval(data['knapsack_array2'])
arr_size2 = 0
knapsack_size = int(data['knapsack_size'])
knapsack_array1 = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0 ,0 ,0,0,0,0]
knapsack_array2 = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0 ,0 ,0,0,0,0]
for i in range(len(array1)):
knapsack_array1[i+1] = int(array1[i])
arr_size1+=1
for i in range(len(array2)):
knapsack_array2[i+1] = int(array2[i])
arr_size2+=1
print(knapsack_array1)
print(knapsack_array2)
simulation_strategy = knpsck.KnapSack(int(knapsack_size), int(arr_size2), knapsack_array1, knapsack_array2)
simulation_context = SimulationContext(simulation_strategy)
data = simulation_context.get_data()
saved = 0
if 'username' in session.keys():
key = db.saved_simulation.find_one({'algo_id': name, "username": session['username'], "type": "idp",
"algo": "knapsack"})
if key is not None:
saved = 1
return render_template(
'/simulation/knapsack_iterative_dp/knapsack_iterative_dp_simulation.html', **locals())
def coin_change_variant5_simulation(name):
data = db.coin_change_variant5.find_one({"_id": ObjectId(name)})
coin_array1 = ast.literal_eval(data['coin_array_variant5'])
coin_array2 = []
coin_array2.append(0)
for i in range(len(coin_array1)):
coin_array2.append(int(coin_array1[i]))
k = len(coin_array1)
payment = data['payment_variant5']
dp_simulation_strategy = ccv5.CoinChangeVariant5(int(payment), k,
coin_array2)
dp_simulation_context = SimulationContext(dp_simulation_strategy)
data = dp_simulation_context.get_data()
saved = 0
if 'username' in session.keys():
key = db.saved_simulation.find_one({
'algo_id': name,
"username": session['username'],
"type": "idp",
"algo": "coin_change_variant5"
})
if key is not None:
saved = 1
return data, saved
def eigen_value_entry_data(data):
data = str(json.dumps(data['entry']))
if eigen_util.validate_entry(data) is False:
return False, data.replace('\"', '').replace('\\r\\n', '\n')
else:
simulation_strategy = eigen.EigenValue(eigen_util.get_matrix(data))
simulation_context = SimulationContext(simulation_strategy)
data = simulation_context.get_data()
posts = db.eigen_value
post_id = posts.insert_one(data).inserted_id
return True, str(post_id)
def gauss_jordan_elimination_entry_data(data):
data = str(json.dumps(data['entry']))
if gj_util.validate_entry(data) is False:
return False, data.replace('\"', '').replace('\\r\\n', '\n')
else:
mat = gj_util.get_matrix(data)
simulation_strategy = gj.GaussJordanElimination(mat)
simulation_context = SimulationContext(simulation_strategy)
data = simulation_context.get_data()
posts = db.gauss_jordan_elimination
post_id = posts.insert_one(data).inserted_id
return True, str(post_id)
def rock_climbing_simulation(name):
data = db.rock_climbing.find_one({"_id": ObjectId(name)})
dp_simulation_strategy = rc.RockClimbing(data['string1'], data['string2'])
dp_simulation_context = SimulationContext(dp_simulation_strategy)
data = dp_simulation_context.get_data()
saved = 0
if 'username' in session.keys():
key = db.saved_simulation.find_one({'algo_id': name, "username": session['username'], "type": "idp",
"algo": "lcs"})
if key is not None:
saved = 1
return data, saved
def shortest_common_supersequence_simulation(name):
data = db.shortest_common_supersequence.find_one({"_id": ObjectId(name)})
dp_simulation_strategy = scs.SCS(data['string1'], data['string2'])
dp_simulation_context = SimulationContext(dp_simulation_strategy)
data = dp_simulation_context.get_data()
saved = 0
if 'username' in session.keys():
key = db.saved_simulation.find_one({'algo_id': name, "username": session['username'], "type": "idp",
"algo": "lcs"})
if key is not None:
saved = 1
return data, saved
def matrix_chain_multiplication_simulation(name):
data = db.matrix_chain_multiplication.find_one({"_id": ObjectId(name)})
dimensions = ast.literal_eval(data['dimension_array'])
k = len(dimensions)
dp_simulation_strategy = mcm.MatrixChainMultiplication(k, dimensions)
dp_simulation_context = SimulationContext(dp_simulation_strategy)
data = dp_simulation_context.get_data()
saved = 0
if 'username' in session.keys():
key = db.saved_simulation.find_one({'algo_id': name, "username": session['username'], "type": "idp",
"algo": "matrix_chain_multiplication"})
if key is not None:
saved = 1
return data, saved
def longest_common_subsequence():
name = '5bd118b776cc9d2f4663873f'
data = db.longest_common_subsequence.find_one({"_id": ObjectId(name)})
simulation_strategy = LCS(data['string1'], data['string2'])
simulation_context = SimulationContext(simulation_strategy)
data = simulation_context.get_data()
saved = 0
if 'username' in session.keys():
key = db.saved_simulation.find_one({
'algo_id': name,
"username": session['username'],
"type": "idp",
"algo": "lcs"
})
if key is not None:
saved = 1
return data, saved
def longest_increasing_subsequence_simulation(name):
data = db.longest_increasing_subsequence.find_one({"_id": ObjectId(name)})
#arr = data['lis_array']
arr = ast.literal_eval(data['lis_array'])
arr_size = 0
lis_array = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0 ,0 ,0,0,0,0]
for i in range(len(arr)):
lis_array[i+1] = int(arr[i])
arr_size+=1
simulation_strategy = lis.LIS(int(arr_size), lis_array)
simulation_context = SimulationContext(simulation_strategy)
data = simulation_context.get_data()
saved = 0
if 'username' in session.keys():
key = db.saved_simulation.find_one({'algo_id': name, "username": session['username'], "type": "idp",
"algo": "lis"})
if key is not None:
saved = 1
return render_template(
'/simulation/lis_iterative_dp/lis_iterative_dp_simulation.html', **locals())
def logistic_regression_data_entry_data(data):
iterations = int(data['iterations'])
steps = int(data['steps'])
alpha = float(data['alpha'])
data = lgr_util.get_data(data['entry'])
x1 = data[0]
x2 = data[1]
y = np.asarray(data[2])
x = lgr_util.combine(x1, x2)
simulation_strategy = lgr.LogisticRegression(x, y, iterations, steps,
alpha)
simulation_context = SimulationContext(simulation_strategy)
data = dict()
data['data'] = simulation_context.get_data()
data['x1'] = x1
data['x2'] = x2
data['y'] = list(y)
data['alpha'] = alpha
posts = db.logistic_regression
post_id = posts.insert_one(data).inserted_id
return post_id
def edit_distance_simulation(name):
data = db.edit_distance.find_one({"_id": ObjectId(name)})
print(data['string1'])
simulation_strategy = edis.EditDistance(data['string1'], data['string2'])
simulation_context = SimulationContext(simulation_strategy)
data = simulation_context.get_data()
saved = 0
if 'username' in session.keys():
key = db.saved_simulation.find_one({
'algo_id': name,
"username": session['username'],
"type": "idp",
"algo": "edit_distance"
})
if key is not None:
saved = 1
return render_template(
'/simulation/edit_distance_iterative_dp/edit_distance_iterative_dp_simulation.html',
**locals())
def k_means_clustering_entry_data(data):
points = km_util.get_points(data['entry'])
max_p = 0.0
min_p = 0.0
for point in points:
for entry in point:
if max_p < entry:
max_p = entry
if min_p > entry:
min_p = entry
centroids = [[random.uniform(min_p, max_p),
random.uniform(min_p, max_p)]
for i in range(int(data['clusters']))]
simulation_strategy = km.KMeans(points, centroids)
simulation_context = SimulationContext(simulation_strategy)
data = dict()
data['data'] = simulation_context.get_data()
posts = db.k_means_clustering
post_id = posts.insert_one(data).inserted_id
return post_id
def linear_regression_data_entry_data(data):
ret_data = dict()
try:
alpha = float(data['alpha'])
except ValueError:
ret_data['data'] = data['entry'].replace('\"', '').replace('\\r\\n', '\n')
ret_data['msg'] = 'Wrong format for Alpha'
return 0, ret_data
try:
iterations = int(data['iterations'])
except ValueError:
ret_data['data'] = data['entry'].replace('\"', '').replace('\\r\\n', '\n')
ret_data['msg'] = 'Wrong format for Iterations'
return 0, ret_data
try:
steps = int(data['steps'])
except ValueError:
ret_data['data'] = data['entry'].replace('\"', '').replace('\\r\\n', '\n')
ret_data['msg'] = 'Wrong format for Steps'
return 0, ret_data
try:
theta0 = float(data['theta0'])
theta1 = float(data['theta1'])
theta = np.array([theta0, theta1]);
except ValueError:
ret_data['data'] = data['entry'].replace('\"', '').replace('\\r\\n', '\n')
ret_data['msg'] = 'Wrong format for Theta'
return 0, ret_data
if steps > iterations:
ret_data['data'] = data['entry'].replace('\"', '').replace('\\r\\n', '\n')
ret_data['msg'] = 'Steps should be less than iterations'
return 0, ret_data
if iterations % steps != 0:
iterations -= iterations % steps
if iterations / steps > 30:
ret_data['data'] = data['entry'].replace('\"', '').replace('\\r\\n', '\n')
ret_data['msg'] = 'Either decrease iterations or increase steps.'
return 0, ret_data
data = str(json.dumps(data['entry'])).replace("\"", "")
if lr_util.validate_entry(data) is False:
ret_data['data'] = data.replace('\"', '').replace('\\r\\n', '\n')
ret_data['msg'] = 'Wrong Data Format'
return 0, ret_data
ret_data['data'] = data.replace('\"', '').replace('\\r\\n', '\n')
ret_data['msg'] = 'Overshoot occurred. Try lower value of Alpha.'
data = lr_util.get_matrix(data)
x = data[0]
y = data[1]
x = np.asarray(x)
y = np.asarray(y)
simulation_strategy = lr.LinearRegression(theta, x, y, alpha, iterations, steps)
simulation_context = SimulationContext(simulation_strategy)
data = simulation_context.get_data()
if data['overshoot']:
return 0, ret_data
posts = db.linear_regression
post_id = posts.insert_one(data).inserted_id
return 1, post_id
def fib():
simulation_strategy = dpfib.Fibonacci(30)
simulation_context = SimulationContext(simulation_strategy)
data = simulation_context.get_data()
return render_template('/simulation/fibonacci_iterative_dp/fibonacci.html',
**locals())