docs: complete for v0.6.3

guofei9987 · Mar 27, 2021 · 3c69c11 · 3c69c11
1 parent 26164eb
commit 3c69c11
Show file tree

Hide file tree

Showing 3 changed files with 58 additions and 24 deletions.
diff --git a/README.md b/README.md
@@ -137,11 +137,8 @@ my_ga = MyGA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, lb=[-1, -10, -
 best_x, best_y = my_ga.run()
 print('best_x:', best_x, '\n', 'best_y:', best_y)
 ```
-## feature2: GPU computation
- We are developing GPU computation, which will be stable on version 1.0.0  
-An example is already available: [https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py)
 
-##  feature3: continue to run
+##  feature2: continue to run
 (New in version 0.3.6)  
 Run an algorithm for 10 iterations, and then run another 20 iterations base on the 10 iterations before:
 ```python
@@ -153,6 +150,21 @@ ga.run(10)
 ga.run(20)
 ```
 
+## feature3: 4-ways to accelerate
+- [x] vectorization
+- [x] multithreading
+- [x] multiprocessing
+- [x] cached
+
+see [https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py)
+
+
+
+## feature4: GPU computation
+ We are developing GPU computation, which will be stable on version 1.0.0  
+An example is already available: [https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py)
+
+
 # Quick start
 
 ## 1. Differential Evolution
@@ -227,19 +239,24 @@ ga = GA(func=schaffer, n_dim=2, size_pop=50, max_iter=800, lb=[-1, -1], ub=[1, 1
 best_x, best_y = ga.run()
 print('best_x:', best_x, '\n', 'best_y:', best_y)
 
-```
+print(ga)
+# # %% Plot the result
+# import pandas as pd
+# import matplotlib.pyplot as plt
+#
+# Y_history = pd.DataFrame(ga.all_history_Y)
+# fig, ax = plt.subplots(2, 1)
+# ax[0].plot(Y_history.index, Y_history.values, '.', color='red')
+# Y_history.min(axis=1).cummin().plot(kind='line')
+# plt.show()
+
+a=1
+if a!=1 :
+    print(1)```
 
 **Step3**: plot the result  
--> Demo code: [examples/demo_ga.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L21)
+-> Demo code: [examples/demo_ga.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#LNone)
 ```python
-import pandas as pd
-import matplotlib.pyplot as plt
-
-Y_history = pd.DataFrame(ga.all_history_Y)
-fig, ax = plt.subplots(2, 1)
-ax[0].plot(Y_history.index, Y_history.values, '.', color='red')
-Y_history.min(axis=1).cummin().plot(kind='line')
-plt.show()
 ```
 
 ![Figure_1-1](https://img1.github.io/heuristic_algorithm/ga_1.png)

diff --git a/docs/en/speed_up.md b/docs/en/speed_up.md
@@ -13,6 +13,7 @@ Totally speaking, **vectorization** is much faster then **parallel**, which is f
 
 To compare the speed of **common**, **vectorization**, **parallel**:
 
+see [/examples/example_function_modes.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py)
 
 ```python
 import numpy as np
@@ -82,9 +83,15 @@ for task_type in ('io_costly', 'cpu_costly'):
 ```
 
 output:
->common mode, time costs:  5.284991  
-vector mode, time costs:  0.608695  
-parallel mode, time costs:  1.114424
+>on io_costly task,use common mode, costs 5.116588s  
+on io_costly task,use multithreading mode, costs 3.113499s  
+on io_costly task,use multiprocessing mode, costs 3.119855s  
+on io_costly task,use vectorization mode, costs 0.604762s  
+on cpu_costly task,use common mode, costs 1.625032s  
+on cpu_costly task,use multithreading mode, costs 1.60131s  
+on cpu_costly task,use multiprocessing mode, costs 1.673792s  
+on cpu_costly task,use vectorization mode, costs 0.192595s  
+
 
 To compare the speed of **common** and **cached**:
 
@@ -119,7 +126,9 @@ print('cache mode, time costs: ', (datetime.datetime.now() - start_time).total_s
 ```
 
 output:
->common mode, time costs:  6.29733  
-cache mode, time costs:  0.308823
+>on io_costly task,use common mode, costs 6.120317s  
+on io_costly task,use cached mode, costs 1.106842s  
+on cpu_costly task,use common mode, costs 1.914744s  
+on cpu_costly task,use cached mode, costs 0.222713s  
 
 
diff --git a/docs/zh/speed_up.md b/docs/zh/speed_up.md
@@ -14,6 +14,7 @@
 
 下面比较 **不加速**、**矢量化**、**多线程**、**多进程** 的性能：
 
+see [/examples/example_function_modes.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py)
 
 ```python
 import numpy as np
@@ -82,9 +83,14 @@ for task_type in ('io_costly', 'cpu_costly'):
 ```
 
 output:
->common mode, time costs:  5.284991  
-vector mode, time costs:  0.608695  
-parallel mode, time costs:  1.114424
+>>on io_costly task,use common mode, costs 5.116588s  
+on io_costly task,use multithreading mode, costs 3.113499s  
+on io_costly task,use multiprocessing mode, costs 3.119855s  
+on io_costly task,use vectorization mode, costs 0.604762s  
+on cpu_costly task,use common mode, costs 1.625032s  
+on cpu_costly task,use multithreading mode, costs 1.60131s  
+on cpu_costly task,use multiprocessing mode, costs 1.673792s  
+on cpu_costly task,use vectorization mode, costs 0.192595s 
 
 
 下面比较  **不加速** 和 **缓存化** 的性能
@@ -119,8 +125,10 @@ print('cache mode, time costs: ', (datetime.datetime.now() - start_time).total_s
 ```
 
 output:
->common mode, time costs:  6.29733  
-cache mode, time costs:  0.308823
+>on io_costly task,use common mode, costs 6.120317s  
+on io_costly task,use cached mode, costs 1.106842s  
+on cpu_costly task,use common mode, costs 1.914744s  
+on cpu_costly task,use cached mode, costs 0.222713s  
 
 
 ## 算子优化加速