i'm working on a voxel raycast based engine. For that i need for each pixel of the screen to run a raycast. To do that, i make a for loop like that :
distanceToScreen = math.tan(math.radians(self.FOV/2))
increment = 1/self.resolution[1]
xScreen = 1/-2
yScreen = 1/-2
for x in range(0,self.resolution[0]):
yScreen = 1/-2
for y in range(0,self.resolution[1]):
info = self.raycast([xScreen,distanceToScreen,yScreen],4)
if info != 0:
'''collect info '''
yScreen+=increment
xScreen+=increment
But this technique is horribly slow. I use pyglet for displaying. Here's also the raycast algorithm :
Step = [0,0,0]
invDir = [0,0,0]
T = [0,0,0]
DeltaT = [0,0,0]
VoxelIncr = [0,0,0]
CurrentV = [self.position[0],self.position[1],self.position[2]]
for i in range(0,3):
if direction[i]!= 0:
Step[i] = int(direction[i]<0) * -1 + int(direction[i]>=0)
T[i] = abs(1/direction[i])
invDir[i] = abs(1/direction[i])
else:
invDir[i] = 100000000000000
T[i] = 100000000000000
DeltaT[i] = invDir[i]
while math.sqrt((CurrentV[0]-int(self.position[0]))**2+(CurrentV[1]-int(self.position[1]))**2+(CurrentV[2]-int(self.position[2]))**2)<=length:
if T[0]<T[1]:
if T[0]<T[2]:
T[0]+=DeltaT[0]
CurrentV[0]+=Step[0]
else:
T[2]+=DeltaT[2]
CurrentV[2]+=Step[2]
else:
if T[1]<T[2]:
T[1]+=DeltaT[1]
CurrentV[1]+=Step[1]
else:
T[2]+=DeltaT[2]
CurrentV[2]+=Step[2]
if int(CurrentV[2]) < len(Map) and int(CurrentV[1]) < len(Map[0]) and int(CurrentV[0]) < len(Map[0][0]) and int(CurrentV[2]) >= 0 and int(CurrentV[1]) >= 0 and int(CurrentV[0]) >= 0 and Map[int(CurrentV[2])][int(CurrentV[1])][int(CurrentV[0])] != 0:
info = Map[int(CurrentV[2])][int(CurrentV[1])][int(CurrentV[0])][1]
break
return info
Related
I'm trying to label BUY, SELL, and HOLD values to the closing stock prices based on the algorithm I found in a paper. I'm not quite able to figure out the error I'm getting. I'd very much appreciate your help. Thank you.
Algorigthm:
[EDITED]
My implementation:
window_size = 11
counter = 0
result = []
window_begin_idx=0; window_end_idx=0; window_middle_idx=0; min_idx=0; max_idx=0;
while counter < len(closing_price):
if counter > window_size:
window_begin_idx = counter - window_size
window_end_idx = window_begin_idx + window_size - 1
window_middle_idx = (window_begin_idx + window_end_idx)//2
for i in range(window_begin_idx, window_end_idx+1):
rng = closing_price[window_begin_idx:window_end_idx+1]
number = closing_price[i]
mins = rng.min()
maxs = rng.max()
if number < mins:
mins=number
min_idx = np.argmin(rng)
if number > maxs:
maxs=number
max_idx = np.argmax(rng)
if max_idx == window_middle_idx:
result.append("SELL")
elif min_idx == window_middle_idx:
result.append("BUY")
else:
result.append("HOLD")
mins = 0.0
maxs = 10000.0
counter+=1
After the edit based on the author's JAVA code, I'm only getting the HOLD label. The author's implementation is here.
You need to initialize mins, maxs, min_idx and max_idx with appropriate values before the main loop.
In your case if max_idx == occurs earlier than any max_idx assignment
Edit after questing change:
Seems in Python you can make similar behavior replacing the whole for-loop with:
rng = closing_price[window_begin_idx:window_end_idx+1]
mins = rng.min()
maxs = rng.max()
min_idx = rng.index(mins)
max_idx = rng.index(maxs)
After reading through the author's implementation and following the suggestions provided by MBo, I have managed to solve this issue. So, now anyone who wants this algorithm in python, below is the code:
window_size = 11
counter = 0
result = []
window_begin_idx=0; window_end_idx=0; window_middle_idx=0; min_idx=0; max_idx=0;
number=0.0; mins=10000.0; maxs=0.0
while counter < len(closing_price):
if counter > window_size:
window_begin_idx = counter - window_size
window_end_idx = window_begin_idx + window_size - 1
window_middle_idx = (window_begin_idx + window_end_idx)//2
for i in range(window_begin_idx, window_end_idx+1):
number = closing_price[i]
if number < mins:
mins=number
min_idx = np.where(closing_price==mins)[0][0]
if number > maxs:
maxs=number
max_idx = np.where(closing_price==maxs)[0][0]
if max_idx == window_middle_idx:
result.append("SELL")
elif min_idx == window_middle_idx:
result.append("BUY")
else:
result.append("HOLD")
mins = 10000.0
maxs = 0.0
counter+=1
i have been given a matrix space of 400,400 with few obstacles inside . I have eight set of actions namely up, down, left right with edge cost of 1 and 4 diagonals with edge cost of 1.42. i took initial node as (5,5) and final node as (195,295). I'm able to reach the goal using Dijkstra's algorithm quite quickly but am not able to backtrack and have been stuck for a long time. Can someone please help me. Here's my code
import numpy as np
import matplotlib.pyplot as plt
import time
import math
blank_list = []
for entries in range(160000):
entries = math.inf
blank_list.append(entries)
initial_matrix = np.array(blank_list, dtype = object).reshape(400, 400)
rows = initial_matrix.shape[0]
cols = initial_matrix.shape[1]
flag = False
# Obstacles
for h in range(0, rows):
for k in range(0, cols):
if (k-225)**2 + (h-50)**2 <= 625:
initial_matrix[h][k] = flag
for h in range(0, rows):
for k in range(0, cols):
if h<= 13*k -140 and h<= 185 and h<= -(7/5)*k +290 and h<= (6/5)*k +30 and h<= -(6/5)*k +210 and h<= k + 100:
initial_matrix[h][k] = flag
for h in range(0, rows):
for k in range(0, cols):
if ((k-150)**2)/1600 + ((h-100)**2)/400 <= 1:
initial_matrix[h][k] = flag
# move subfunctions
def move_right(i,j, map_):
if not map_[i,j+1]:
return None
if j == 300:
return None
else:
cost = initial_matrix[i][j] + 1
j = j + 1
return (i,j) , cost
def move_left(i,j,map_):
if not map_[i,j-1]:
return None
if j == 0:
return None
else:
cost = initial_matrix[i][j] + 1
j = j - 1
return (i,j) , cost
def move_up(i,j,map_):
if not map_[i-1,j]:
return None
if i == 0:
return None
else:
cost = initial_matrix[i][j] + 1
i = i - 1
return (i,j) , cost
def move_down(i,j,map_):
if not map_[i+1,j]:
return None
if i == 200:
return None
else:
cost = initial_matrix[i][j] + 1
i = i + 1
return (i,j) , cost
def move_upright(i,j,map_):
if not map_[i-1,j+1]:
return None
if j == 300 or i == 0:
return None
else:
cost = 1.42 + initial_matrix[i][j]
j = j + 1
i = i - 1
return (i,j) , cost
def move_downright(i,j,map_):
if not map_[i+1,j+1]:
return None
if j == 300 or i == 200:
return None
else:
cost = 1.42 + initial_matrix[i][j]
j = j + 1
i = i + 1
return (i,j) , cost
def move_upleft(i,j,map_):
if map_[i-1,j-1]:
return None
if j == 0 or i == 0:
return None
else:
cost = 1.42 + initial_matrix[i][j]
j = j - 1
i = i - 1
return (i,j) , cost
def move_downleft(i,j,map_):
if not map_[i+1,j-1]:
return None
if j == 0 or i == 200:
return None
else:
cost = 1.42 + initial_matrix[i][j]
j = j - 1
i = i + 1
return (i,j) , cost
def get_neighbours(i,j):
neighbours_cost = []
index = []
action_up = move_up(i,j,initial_matrix)
if action_up is not None:
neighbours_cost.append(action_up[1])
index.append(action_up[0])
action_down = move_down(i,j,initial_matrix)
if action_down is not None:
neighbours_cost.append(action_down[1])
index.append(action_down[0])
action_left = move_left(i,j,initial_matrix)
if action_left is not None:
neighbours_cost.append(action_left[1])
index.append(action_left[0])
action_right = move_right(i,j,initial_matrix)
if action_right is not None:
neighbours_cost.append(action_right[1])
index.append(action_right[0])
action_upright = move_upright(i,j,initial_matrix)
if action_upright is not None:
neighbours_cost.append(action_upright[1])
index.append(action_upright[0])
action_downright = move_downright(i,j,initial_matrix)
if action_downright is not None:
neighbours_cost.append(action_downright[1])
index.append(action_downright[0])
action_upleft = move_upleft(i,j,initial_matrix)
if action_upleft is not None:
neighbours_cost.append(action_upleft[1])
index.append(action_upleft[0])
action_downleft = move_downleft(i,j,initial_matrix)
if action_downleft is not None:
neighbours_cost.append(action_downleft[1])
index.append(action_downleft[0])
return neighbours_cost, index
'''def get_current_value(i,j):
current_value = initial_matrix[i][j]
return current_value'''
'''def locate_value_index(mat):
i,j = np.where(mat == node)
i = int(i)
j = int(j)
return (i,j)'''
def sort_list1(list_a,list_b):
list_b = [i[1] for i in sorted(zip(list_a, list_b))]
def sort_list2(list_a):
list_a.sort()
goal = (195,295)
ind = (5,5)
initial_matrix[ind[0]][ind[1]] = 0
node_cost = 0
explore_queue = []
index_queue = []
index_queue.append(ind)
explore_queue.append(node_cost)
visited = set()
breakwhile = False
start_time = time.clock()
while len(index_queue) != 0 and not breakwhile:
node = index_queue[0]
visited.add(node)
#for i in range(len(index_queue)):
index_queue.pop(0)
explore_queue.pop(0)
#print(visited)
#val = get_current_value(ind[0],ind[1])
pair = get_neighbours(node[0],node[1])
neighbours_cost = pair[0]
index = pair[1]
#print(index)
#print(neighbours_cost)
#######
if node == goal:
print('goal reached')
breakwhile = True
#######
for i in range(len(index)):
if index[i] not in visited:
old_cost = initial_matrix[index[i][0]][index[i][1]]
if neighbours_cost[i] < old_cost:
initial_matrix[index[i][0]][index[i][1]] = neighbours_cost[i]
if old_cost != math.inf:
ind_node = index_queue.index((index[i][0], index[i][1]))
index_queue.pop(ind_node)
explore_queue.pop(ind_node)
index_queue.append(index[i])
explore_queue.append(initial_matrix[index[i][0]][index[i][1]])
sort_list1(explore_queue,index_queue)
sort_list2(explore_queue)
end_time = time.clock()
print(end_time - start_time)
I figured out the solution. it is to create dictionary with all the index values and iterating them over a while loop
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import linalg, optimize
%matplotlib inline
Data load
data = pd.read_csv("D:/Stat/TimeSeries/KRW_month_0617_1.csv",index_col="Date") / 100
para = open("D:/Stat/TimeSeries/KRW_month_0617_1.txt").readlines()[0:2]
data.index = pd.to_datetime(data.index)
Parameters
cond = []
params = []
time = []
for i in para:
j = i.split()
for k in j:
cond.append(k)
cond = cond[1:]
for i in range(len(cond)):
cond[i] = round(float(cond[i]),4)
params = cond[0:23]
time = cond[23:]
maturity = np.array(time[1:])
timegap = 1/cond[23]
Functions We need
def Paramcheck(Params, checkStationary = 1):
result = 0
Kappa = np.array([[params[20],0,0], [0,params[21],0], [0,0,params[22]]])
Sigma = np.array([[params[1],0,0], [params[2],params[3],0], [params[4],params[5],params[6]]])
State = np.array([params[7], params[8], params[9]])
Lambda = params[0]
SigmaEps = np.identity(10)
for i in range(10):
SigmaEps[i][i] = params[i+10]
for i in range(len(Sigma)):
if Sigma[i][i] < 0:
result = 1
for j in SigmaEps:
if np.any(SigmaEps) < 0:
result = 1
if Lambda < 0.05 or Lambda > 2:
result = 1
elif State[0] < 0:
result = 1
elif Kappa[0][0] < 0:
result = 1
if result == 0 and checkStationary > 0:
if max(np.linalg.eigvals(-Kappa).real) > 0:
result = 2
return result
def CheckDet(x):
if x == np.inf or x == np.nan:
result = 1
elif x < 0:
result = 2
elif abs(x) < 10**-250:
result = 3
else:
result = 0
return result
def NS_factor(lambda_val, maturity):
col1 = np.ones(len(maturity))
col2 = (1 - np.exp(-lambda_val*maturity))/(lambda_val*maturity)
col3 = col2 - np.exp(-lambda_val*maturity)
factor = np.array([col1,col2,col3]).transpose()
return factor
def DNS_Kalman_filter(Params, *args):
N = Paramcheck(Params)
if N == 0:
Kappa = np.array([[params[20],0,0], [0,params[21],0], [0,0,params[22]]])
Sigma = np.array([[params[1],0,0], [params[2],params[3],0],
[params[4],params[5],params[6]]])
State = np.array([params[7], params[8], params[9]])
Lambda = params[0]
SigmaEps = np.identity(10)
for i in range(10):
SigmaEps[i][i] = params[i+10]
Obs_Yield = args[0]
Obs_Date = args[1]
Timegap = args[2]
Obs_Mty = args[3]
Finalstate = args[4]
Mty_length = len(Obs_Mty)
B = NS_factor(lambda_val = Lambda,maturity = Obs_Mty)
H_large = SigmaEps **2
N_obs = len(Obs_Date)
LLH_vec = np.zeros(N_obs)
phi1 = linalg.expm(-Kappa*Timegap)
phi0 = (np.identity(3)-phi1) # State
Eigenvalues = np.linalg.eig(Kappa)[0]
Eigen_vec = np.linalg.eig(Kappa)[1]
Eigen_vec_inv = np.linalg.inv(Eigen_vec)
S = Eigen_vec_inv # Sigma # Sigma.transpose() # Eigen_vec_inv.transpose()
Atilde = np.dot(Sigma[0], Sigma[0])
Btilde = np.dot(Sigma[1], Sigma[1])
Ctilde = np.dot(Sigma[2], Sigma[2])
Dtilde = np.dot(Sigma[0], Sigma[1])
Etilde = np.dot(Sigma[0], Sigma[2])
Ftilde = np.dot(Sigma[1], Sigma[2])
res1= Atilde* Obs_Mty* Obs_Mty/6
res2= Btilde*(1/(2*Lambda**2) - (1-np.exp(-Lambda*Obs_Mty))/(Lambda**3*Obs_Mty) + (1-
np.exp(-2*Lambda*Obs_Mty))/(4*Lambda**3*Obs_Mty))
res3= Ctilde*(1/(2*Lambda**2) + np.exp(-Lambda*Obs_Mty)/(Lambda**2)-
Obs_Mty*np.exp(-2*Lambda*Obs_Mty)/(4*Lambda) -
3*np.exp(-2*Lambda*Obs_Mty)/(4*Lambda**2) - 2*(1-np.exp(-
Lambda*Obs_Mty))/(Lambda**3*Obs_Mty) + 5*(1-
np.exp(-2*Lambda*Obs_Mty))/(8*Lambda**3*Obs_Mty))
res4= Dtilde*(Obs_Mty/(2*Lambda) + np.exp(-Lambda*Obs_Mty)/(Lambda**2) - (1-np.exp(-
Lambda*Obs_Mty))/(Lambda**3*Obs_Mty))
res5= Etilde*(3*np.exp(-Lambda*Obs_Mty)/(Lambda**2) + Obs_Mty/(2*Lambda)+Obs_Mty*np.exp(-
Lambda*Obs_Mty)/(Lambda) - 3*(1-np.exp(-Lambda*Obs_Mty))/(Lambda**3*Obs_Mty))
res6= Ftilde*(1/(Lambda**2) + np.exp(-Lambda*Obs_Mty)/(Lambda**2) -
np.exp(-2*Lambda*Obs_Mty)/(2*Lambda**2) - 3*(1-np.exp(-
Lambda*Obs_Mty))/(Lambda**3*Obs_Mty) + 3*(1-
np.exp(-2*Lambda*Obs_Mty))/(4*Lambda**3*Obs_Mty))
val = res1 + res2 + res3 + res4 + res5 + res6
V_mat = np.zeros([3,3])
V_lim = np.zeros([3,3])
for i in range(3):
for j in range(3):
V_mat[i][j] = S[i][j]*(1-np.exp(-(Eigenvalues[i] +
Eigenvalues[j])*Timegap))/(Eigenvalues[i] + Eigenvalues[j])
V_lim[i][j] = S[i][j]/(Eigenvalues[i] + Eigenvalues[j])
Q = (Eigen_vec # V_mat # Eigen_vec.transpose()).real
Sigma_lim = (Eigen_vec # V_lim # Eigen_vec.transpose()).real
for i in range(N_obs):
y = Obs_Yield[i]
xhat = phi0 + phi1 # State
y_implied = B # xhat
v = y - y_implied + val
Sigmahat = phi1 # Sigma_lim # phi1.transpose() + Q
F = B # Sigmahat # B.transpose() + H_large
detF = np.linalg.det(F)
if CheckDet(detF) > 0:
N = 3
break
Finv = np.linalg.inv(F)
State = xhat + Sigmahat # B.transpose() # Finv # v
Sigma_lim = Sigmahat - Sigmahat # B.transpose() # Finv # B # Sigmahat
LLH_vec[i] = np.log(detF) + v.transpose() # Finv # v
if N == 0:
if Finalstate:
yDate = Obs_Date[-1]
result = np.array([yDate,State])
else:
result = 0.5 * (sum(LLH_vec) + Mty_length*N_obs*np.log(2*np.pi))
else:
result = 7000000
return result
I made a code that does Arbitrage Free Nelson-Siegel model. Data is return rates of bond (1Y,1.5Y, ... ,20Y). I wanna optimize that function with scipy optimize.minimize function with fixed *args.
Suppose that Initial parmas are verified that it's close to optimized params from empirical experiments using Dynamic Nelson-Siegel Model.
LLC_new = 0
while True:
LLC_old = LLC_new
OPT = optimize.minimize(x0=params,fun=DNS_Kalman_filter, args=
(data.values,data.index,timegap,maturity,0))
params = OPT.x
LLC_new = round(OPT.fun,5)
print("Current LLC: %0.5f" %LLC_new)
if LLC_old == LLC_new:
OPT_para = params
FinalState = DNS_Kalman_filter(params,data.values,data.index,timegap,maturity,True)
break
Result is
Current LLC: -7613.70146
Current LLC: -7613.70146
LLC(log-likelihood value) isn't maximized. It's not a result I desire using Optimizer.
Is there any solution for that?
In R, there is optim() function works as similar as scipy.optimize.minimize() which works really well. I also have a R code for that very similar to this Python code.
There are no errors in the code execution , but the output of the minimax algorithm in incorrect , please have a look,`
the AI_makemove function is called from the main loop, and the board_state is the copy of the actual board.
The function AI_makemove is supposed to return the best move for the computer against the user , board_state is the current state of the board, depth is the number of positions filled in the board , check_if_won function returns true if the state is a win state for the current player .
def AI_makemove(board_state , isAI , depth):
temp_board = copy.deepcopy(board_state)
depth+=1
print(temp_board , depth , isAI)
if isAI:
bestVal = -9999
a = b = 0
for i in range(0,3):
for j in range(0,3):
if temp_board[i][j] == 0:
temp_board1 = copy.deepcopy(temp_board)
temp_board1[i][j] = 2
if check_if_won(2,temp_board1):
return [1 , i, j]
if depth == 9:
return [bestVal , a ,b]
l = AI_makemove(temp_board1,False,depth)
if int(l[0]) > bestVal:
bestVal = int(l[0])
a = int(l[1])
b = int(l[2])
else:
bestVal = +9999
a = b = 0
for i in range(0, 3):
for j in range(0, 3):
if temp_board[i][j] == 0:
temp_board1 = copy.deepcopy(temp_board)
temp_board1[i][j] = 1
if check_if_won(1,temp_board1):
return [-1 , i, j]
if depth == 9:
return [bestVal , a ,b]
l = AI_makemove(temp_board1,True,depth)
if int(l[0]) < bestVal:
bestVal = int(l[0])
a = int(l[1])
b = int(l[2])
return [bestVal , a ,b]
I tried a couple of times to debug the code , but wasn't able to fix it , so i wrote the code again with a different approach and it worked. Here's the code
I am struggling with a very simple loop that perfectly works if run "standalone", but doesn't work anymore if I use it as an outer loop for many other instructions (which also perfectly work if run standalone for only 1 iteration).
The simple outer loop is a
for i in range(0,somevalue):
do some inner instructions
Here is the full code, which perfectly works if I put a range of dimension 1, whilst it never ends if I put even a simple range of dimension 2:
import numpy as np
import numpy.ma as ma
import random
import matplotlib.pyplot as plt
i = int
x = np.zeros(1440)
class_x = np.zeros(1440)
w1 = np.array([0,6*60])
w2 = np.array([20*60,23*60])
x[w1[0]:(w1[1])] = np.full(np.diff(w1),0.001)
x[w2[0]:(w2[1])] = np.full(np.diff(w2),0.001)
x_masked = np.zeros_like(ma.masked_not_equal(x,0.001))
c = 10
func_time = 300
max_free_spot = int
i = 0
for i in range(0,1):
tot_time = 0
switch_count = 0
switch_ons = []
while
tot_time <= func_time:
switch_on = random.choice([random.randint(w1[0],(w1[1]-c)),random.randint(w2[0],(w2[1]-c))])
if x[switch_on] == 0.001:
if switch_on in range(w1[0],w1[1]):
if np.any(x[switch_on:w1[1]]!=0.001):
next_switch = [switch_on + k[0] for k in np.where(x[switch_on:]!=0.001)]
if (next_switch[0] - switch_on) >= c and max_free_spot >= c:
upper_limit = min((next_switch[0]-switch_on),min(func_time,w1[1]-switch_on))
elif (next_switch[0] - switch_on) < c and max_free_spot >= c:
continue
else: upper_limit = next_switch[0]-switch_on
else:
upper_limit = min(func_time,w1[1]-switch_on) #max random length of cycle
if upper_limit >= c:
indexes = np.arange(switch_on,switch_on+(random.randint(c,upper_limit)))
else:
indexes = np.arange(switch_on,switch_on+upper_limit)
else:
if np.any(x[switch_on:w2[1]]!=0.001):
next_switch = [switch_on + k[0] for k in np.where(x[switch_on:]!=0.001)]
if (next_switch[0] - switch_on) >= c:
upper_limit = min((next_switch[0]-switch_on),min(func_time,w2[1]-switch_on))
elif (next_switch[0] - switch_on) < c and max_free_spot >= c:
continue
else: upper_limit = next_switch[0]-switch_on
else:
upper_limit = min(func_time,w2[1]-switch_on)
if upper_limit >= c:
indexes = np.arange(switch_on,switch_on+(random.randint(c,upper_limit)))
else:
indexes = np.arange(switch_on,switch_on+upper_limit)
tot_time = tot_time + indexes.size
switch_ons.append(switch_on)
if tot_time > func_time:
indexes_adj = indexes[:-(tot_time-func_time)]
coincidence = random.randint(1,5)
np.put(x_masked,indexes_adj,(2*coincidence),mode='clip')
np.put(x,indexes_adj,(2*coincidence))
x_masked = np.zeros_like(ma.masked_greater_equal(x_masked,0.001))
tot_time = (tot_time - indexes.size) + indexes_adj.size
switch_count = switch_count + 1
break
else:
coincidence = random.randint(1,5)
np.put(x_masked,indexes,(2*coincidence),mode='clip')
np.put(x,indexes,(2*coincidence))
x_masked = np.zeros_like(ma.masked_greater_equal(x_masked,0.001))
tot_time = tot_time
switch_count = switch_count + 1
free_spots = []
for j in ma.notmasked_contiguous(x_masked):
free_spots.append(j.stop-j.start)
max_free_spot = max(free_spots)
class_x = class_x + x
plt.plot(class_x)
Any help is really really appreciated