change varable num to num_location

README.md

@@ -31,16 +31,16 @@ numpy==1.11.1, python2/3的環境下皆可運行
 
 ### Annealing parameters
 
-    markov_step = 10 * num	# 內循環次數
-    T = 100					# 初始溫度
-    T = 1					# 最低溫度
-    T_ALPHA = 0.99			# 退溫常數
+    markov_step = 10 * num_location	# 內循環次數
+    T = 100					        # 初始溫度
+    T = 1					        # 最低溫度
+    T_ALPHA = 0.99			        # 退溫常數
 
 因為是隨機搜尋演算法，不一定能保證每次都得到最佳解答
 
 如果想要得到更好的解，增加markov_step是個好辦法
 
-	markov_step = 100 * num # 內循環次數
+    markov_step = 100 * num_location # 內循環次數
 
 ### Execute
 直接執行`python tsp.py`, 預設會讀取`data/pokestops.csv`

tsp.py

@@ -1,24 +1,24 @@
 #coding:utf-8
-import numpy as np
+import num_locationpy as np
 import sqlite3
 import json
 from util import plot
 
 def sum_distmat(p, distmat):
     dist = 0
-    num = p.shape[0]
-    for i in range(num-1):
+    num_location = p.shape[0]
+    for i in range(num_location-1):
         dist += distmat[p[i]][p[i+1]]
-    dist += distmat[p[0]][p[num-1]]
+    dist += distmat[p[0]][p[num_location-1]]
     return dist
 
 def get_distmat(p):
-    num = p.shape[0]
+    num_location = p.shape[0]
     # 1 degree of lat/lon ~ 111km = 111000m in Taiwan
     p *= 111000
-    distmat = np.zeros((num, num))
-    for i in range(num):
-        for j in range(i, num):
+    distmat = np.zeros((num_location, num_location))
+    for i in range(num_location):
+        for j in range(i, num_location):
             distmat[i][j] = distmat[j][i] = np.linalg.norm(p[i] - p[j])
     return distmat
 
@@ -78,15 +78,15 @@ def main():
     coordinates = np.loadtxt(filename, delimiter=',')
 
     # Params Initial
-    num = coordinates.shape[0]
-    markov_step = 10 * num
+    num_location = coordinates.shape[0]
+    markov_step = 10 * num_location
     T, T_MIN, T_ALPHA  = 100, 1, 0.99
 
     # Build distance matrix to accelerate cost computing
     distmat = get_distmat(coordinates)
 
     # States: New, Current and Best
-    sol_new, sol_current, sol_best = (np.arange(num), ) * 3
+    sol_new, sol_current, sol_best = (np.arange(num_location), ) * 3
     cost_new, cost_current, cost_best = (float('inf'), ) * 3
 
     # Record costs during the process