mirror of
https://github.com/LmeSzinc/AzurLaneAutoScript.git
synced 2025-01-09 10:17:38 +08:00
Upd: Research preset using E2 projects and research_optimizer
This commit is contained in:
LmeSzinc
parent
bad2995982
commit
e3fdef94bb
@ -55,8 +55,8 @@ PROJECT_TABLE = """
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34 4 Marcopolo-2.5 766.6697864 0 0 0 0 2.25 0.04
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35 4 Marcopolo-5 485.7871007 0 0 0 0 3.75 0.06
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36 4 Marcopolo-8 200.3313937 0 0 0 0 6 0.096
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37 4 EB-2 203.9216684 0 0 0 0 0 0.024
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38 4 EP-2 203.9216684 0 0 0 0 0 0.06
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37 4 Z-2 203.9216684 0 0 0 0 0 0.024
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38 4 A-2 203.9216684 0 0 0 0 0 0.06
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39 4 G-1.5 582.001119 0.104 0.104 0.299 0.299 0.299 0.025
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40 4 G-2.5 402.5500509 0.135 0.135 0.403333333 0.403333333 0.403333333 0.04
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41 4 G-4 305.1449135 0.2585 0.2585 0.723333333 0.723333333 0.723333333 0.12
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@ -111,8 +111,8 @@ PROJECT_TABLE = """
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90 2 C-6 331.5425296 0 0 0 0 0 0
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91 2 C-8 224.3791833 0 0 0 0 0 0
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92 2 C-12 170.3707535 0 0 0 0 0 0
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93 2 EB-2 222.8618262 0 0 0 0 0 0
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94 2 EP-2 222.8618262 0 0 0 0 0 0
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93 2 Z-2 222.8618262 0 0 0 0 0 0
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94 2 A-2 222.8618262 0 0 0 0 0 0
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95 2 G-1.5 636.0571355 0 0 0 0 0 0
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96 2 G-2.5 439.9387285 0 0 0 0 0 0
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97 2 G-4 333.4866434 0 0 0 0 0 0
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@ -167,8 +167,8 @@ PROJECT_TABLE = """
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146 3 C-6 328.1807665 0 0 0 0 0 0
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147 3 C-8 222.1040313 0 0 0 0 0 0
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148 3 C-12 168.6432343 0 0 0 0 0 0
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149 3 EB-2 220.6020598 0 0 0 0 0 0
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150 3 EP-2 220.6020598 0 0 0 0 0 0
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149 3 Z-2 220.6020598 0 0 0 0 0 0
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150 3 A-2 220.6020598 0 0 0 0 0 0
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151 3 G-1.5 629.6076661 0 0 0 0 0 0
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152 3 G-2.5 435.4778534 0 0 0 0 0 0
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153 3 G-4 330.1051674 0 0 0 0 0 0
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@ -245,8 +245,8 @@ PROJECT_TABLE_S4 = """
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34 4 Marcopolo-2.5 766.6697864 0 0 0 0 2.25 0.04
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35 4 Marcopolo-5 485.7871007 0 0 0 0 3.75 0.06
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36 4 Marcopolo-8 200.3313937 0 0 0 0 6 0.096
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37 4 EB-2 647.3855544 0 0 0 0 0 0.024
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38 4 EP-2 647.3855544 0 0 0 0 0 0.06
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37 4 Z-2 647.3855544 0 0 0 0 0 0.024
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38 4 A-2 647.3855544 0 0 0 0 0 0.06
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39 4 G-1.5 1847.665921 0.104 0.104 0.299 0.299 0.299 0.025
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40 4 G-2.5 1277.966633 0.135 0.135 0.403333333 0.403333333 0.403333333 0.04
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41 4 G-4 968.7367243 0.2585 0.2585 0.723333333 0.723333333 0.723333333 0.12
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@ -646,7 +646,7 @@ FILTER_REGEX = re.compile('([s\!][1234])?'
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'|gascogne|champagne|cheshire|drake|mainz|odin'
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'|anchorage|hakuryu|agir|august|marcopolo)?'
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'(dr|pry)?'
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'([bcdeghqt])?'
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'([bcdeghqtaz])?'
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'-?'
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'(\d.\d|\d\d?)?')
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FILTER_ATTR = ('series', 'ship', 'ship_rarity', 'genre', 'duration')
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@ -968,6 +968,8 @@ class FilterSimulator:
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target = np.array([513, 513, 343, 343, 343, 150])
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def __init__(self, string):
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string = string.replace('E-315', 'A2')
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string = string.replace('E-031', 'Z2')
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self.string = string
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self.pool = ResearchPool(string)
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@ -1007,15 +1009,20 @@ def join_filter(selection):
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return ' > '.join(selection)
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def beautify_filter(string):
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if isinstance(string, str):
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string = split_filter(string)
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out = ''
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for index in range(0, len(string), 8):
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row = string[index:index + 8]
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out += ' > ' + join_filter(row) + '\n'
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out = '\n ' + out.strip('> ')
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return out
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def beautify_filter(list_filter):
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if isinstance(list_filter, str):
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list_filter = split_filter(list_filter)
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out = []
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length = 0
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for selection in list_filter:
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if length + len(selection) + 3 > 70:
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out.append('\n')
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length = 0
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out.append(selection)
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length += len(selection) + 3
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string = ' > '.join(out).strip('\n >').replace(' > \n', '\n').replace('\n ', '\n')
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return string
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def position_change(string, position):
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@ -1118,7 +1125,7 @@ class BruteForceOptimizer:
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# 切魔方：'B > T > E'
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# 只做0.5h魔方：'B > T > E > H1 > H2 > H4'
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# 不切魔方：'B > T > E > H'
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ResearchPool.remove_projects = 'B > T > E > H1 > H2 > H4'
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ResearchPool.remove_projects = 'B > T > H1 > H2 > H4'
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# 每日活跃时间，按天计算
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# 超出活跃时间后，仍在挂项目，但不再开始新项目
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FilterSimulator.active = 24 / 24
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@ -1140,7 +1147,7 @@ if __name__ == '__main__':
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"""
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这个文件包含模拟器和优化器两部分，取消注释对应的代码来运行
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Alas用户运行需要额外安装numba，无指定版本
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非Alas用户运行需要python>=3.7，安装 numba numpy tqdm, 无指定版本
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非Alas用户运行需要python>=3.7，安装 numba==0.45.1 llvmlite==0.29.0 numpy tqdm
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过滤器与Alas内的过滤器基本相同，编写参考 https://github.com/LmeSzinc/AzurLaneAutoScript/wiki/filter_string_cn
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但需要注意：
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@ -1162,23 +1169,27 @@ if __name__ == '__main__':
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模拟大量用户使用同一个过滤器的平均毕业时间和毕业时获取物品的平均数量
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取消注释这些代码，将你的过滤器粘贴至这里，并运行，在8700k上需要约4.5分钟
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"""
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simulator = FilterSimulator("""
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S4-DR0.5 > S4-PRY0.5 > S4-H0.5 > S4-Q0.5 > S4-DR2.5 > !4-0.5 > S4-G1.5 > S4-Q1
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> S4-DR5 > S4-DR8 > S4-G4 > S4-PRY2.5 > !4-1 > S4-Q2 > reset > S4-G2.5
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> S4-PRY5 > S4-PRY8 > !4-2 > !4-1.5 > S4-Q4 > !4-2.5 > !4-4 > S4-C6
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> S4-C8 > !4-6 > !4-8 > !4-12 > S4-C12
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""")
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simulator.run(sample_count=100000)
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# simulator = FilterSimulator("""
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# S4-DR0.5 > S4-PRY0.5 > S4-Q0.5 > S4-H0.5 > Q0.5 > S4-DR2.5
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# > S4-G1.5 > S4-Q1 > S4-DR5 > 0.5 > S4-G4 > S4-Q2 > S4-PRY2.5 > reset
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# > S4-DR8 > Q1 > 1 > S4-E-315 > S4-G2.5 > G1.5 > 1.5 > S4-E-031
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# > S4-Q4 > Q2 > E2 > 2 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S4-PRY5
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# > S4-PRY8 > Q4 > G4 > 4 > S4-C6 > DR5 > PRY5 > 5 > C6 > 6 > S4-C8
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# > S4-C12 > DR8 > PRY8 > C8 > 8 > C12 > 12
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# """)
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# simulator.run(sample_count=300000)
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"""
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优化一个过滤器，尝试调整过滤器选择的顺序，找到满足目标条件的消耗时间最短的排列方式
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类似于早期机器学习的实现，收敛过程中，向前尝试移动的距离变短，模拟样本量增大
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取消注释这些代码并运行，在8700k上需要约1-2天
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已给出一个包含所有选项、顺序大体正确的过滤器作为开始，不需要修改
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"""
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# optimizer = BruteForceOptimizer()
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# optimizer.optimize("""
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# S4-H0.5 > S4-DR0.5 > S4-PRY0.5 > S4-Q0.5 > !4-0.5 > S4-G1.5 > S4-Q1 > S4-DR2.5
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# > S4-G4 > S4-Q4 > S4-DR5 > S4-DR8 > S4-Q2 > S4-PRY2.5 > S4-G2.5 > !4-1
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# > reset > S4-PRY8 > !4-1.5 > S4-PRY5 > !4-2.5 > !4-2 > !4-4
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# > S4-C6 > !4-C8 > S4-C8 > !4-C6 > S4-C12 > !4-C12
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# """, diff=1)
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optimizer = BruteForceOptimizer()
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optimizer.optimize("""
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S4-H0.5 > S4-DR0.5 > S4-PRY0.5 > S4-Q0.5 > !4-0.5 > S4-G1.5 > S4-Q1 > S4-DR2.5
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> S4-G4 > S4-Q4 > S4-DR5 > S4-DR8 > S4-Q2 > S4-PRY2.5 > S4-G2.5 > !4-1
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> S4-H1 > S4-H2 > S4-H4
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> S4-EP2 > S4-EB2
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> reset > S4-PRY8 > !4-1.5 > S4-PRY5 > !4-2.5 > !4-2 > !4-4
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> S4-C6 > !4-C8 > S4-C8 > !4-C6 > S4-C12 > !4-C12
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""", diff=1)
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93
module/config/code_generator.py
Normal file
93
module/config/code_generator.py
Normal file
@ -0,0 +1,93 @@
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import typing as t
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class TabWrapper:
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def __init__(self, generator, prefix='', suffix=''):
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"""
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Args:
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generator (CodeGenerator):
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"""
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self.generator = generator
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self.prefix = prefix
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self.suffix = suffix
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def __enter__(self):
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if self.prefix:
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self.generator.add(self.prefix)
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self.generator.tab_count += 1
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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self.generator.tab_count -= 1
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if self.suffix:
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self.generator.add(self.suffix)
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class CodeGenerator:
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def __init__(self):
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self.tab_count = 0
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self.lines = []
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def generate(self) -> t.Iterable[str]:
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yield ''
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def add(self, line, comment=False, newline=True):
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self.lines.append(self._line_with_tabs(line, comment=comment, newline=newline))
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def print(self):
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lines = ''.join(self.lines)
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print(lines)
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def write(self, file: str = None):
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lines = ''.join(self.lines)
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with open(file, 'w', encoding='utf-8', newline='') as f:
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f.write(lines)
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def _line_with_tabs(self, line, comment=False, newline=True):
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if comment:
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line = '# ' + line
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out = ' ' * self.tab_count + line
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if newline:
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out += '\n'
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return out
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def _repr(self, obj):
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if isinstance(obj, str) and '\n' in obj:
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out = '"""\n'
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with self.tab():
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for line in obj.strip().split('\n'):
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line = line.strip()
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out += self._line_with_tabs(line)
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out += self._line_with_tabs('"""', newline=False)
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return out
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return repr(obj)
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def tab(self):
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return TabWrapper(self)
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def Value(self, key=None, value=None, **kwargs):
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if key is not None:
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self.add(f'{key} = {self._repr(value)}')
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for key, value in kwargs.items():
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self.Value(key, value)
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def Comment(self, text):
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for line in text.strip().split('\n'):
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line = line.strip()
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self.add(line, comment=True)
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def Dict(self, key):
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return TabWrapper(self, prefix=str(key) + ' = {', suffix='}')
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def DictItem(self, key=None, value=None, **kwargs):
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if key is not None:
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self.add(f'{self._repr(key)}: {self._repr(value)},')
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for key, value in kwargs.items():
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self.DictItem(key, value)
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generator = CodeGenerator()
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Value = generator.Value
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Comment = generator.Comment
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Dict = generator.Dict
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DictItem = generator.DictItem
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@ -1,111 +1,276 @@
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FILTER_STRING_SHORTEST = '0.5 > 1 > 1.5 > 2 > 2.5 > 3 > 4 > 5 > 6 > 8 > 10 > 12'
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FILTER_STRING_CHEAPEST = 'Q1 > Q2 > T3 > T4 > Q4 > C6 > T6 > C8 > C12 > G1.5 > D2.5 > G2.5 > D5 > Q0.5 > G4 > D8 > H1 > H2 > H0.5 > D0.5 > H4'
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DICT_FILTER_PRESET = {
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'series_5_blueprint_152_cube': """
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S5-Q0.5 > S5-DR0.5 > S5-PRY0.5 > S5-H0.5 > S5-DR2.5 > 0.5 > S5-Q1
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> S5-H1 > S5-Q2 > reset > S5-Q4 > S5-G1.5 > Q1 > 1 > S5-H2 > S5-G4
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> 1.5 > S5-G2.5 > S5-PRY2.5 > 2.5 > S5-DR5 > Q2 > 2 > 3
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> S5-H4 > S5-DR8 > S5-PRY5 > Q4 > G4 > 4 > 5 > S5-PRY8
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> S5-C6 > C6 > 6 > S5-C8 > 8 > S5-C12 > 12""",
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'series_5_blueprint_152': """
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S5-DR0.5 > S5-PRY0.5 > S5-H0.5 > S5-Q0.5 > S5-DR2.5 > 0.5 > S5-G1.5
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> S5-Q1 > S5-DR5 > S5-DR8 > S5-G4 > S5-PRY2.5 > 1 > S5-Q2 > reset
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> S5-G2.5 > S5-PRY5 > S5-PRY8 > 1.5 > 2 > S5-Q4 > 2.5 > 3
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> Q4 > G4 > 4 > 5 > S5-C6 > C6 > 6 > S5-C8 > 8
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> S5-C12 > 12""",
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'series_5_blueprint_only_cube': """
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S5-DR0.5 > S5-H0.5 > S5-PRY0.5 > S5-H1 > S5-H2 > S5-DR2.5 > S5-Q0.5
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> 0.5 > S5-DR5 > reset > S5-DR8 > S5-H4 > S5-Q1 > Q1 > 1 > S5-G1.5
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> 1.5 > S5-Q2 > Q2 > 2 > S5-G2.5 > S5-PRY2.5 > 2.5 > 3
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> S5-Q4 > S5-G4 > Q4 > G4 > 4 > S5-PRY5 > 5 > S5-PRY8 > S5-C6
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> C6 > 6 > S5-C8 > 8 > S5-C12 > 12""",
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'series_5_blueprint_only': """
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S5-DR0.5 > S5-PRY0.5 > S5-H0.5 > S5-DR8 > S5-DR2.5 > S5-DR5 > S5-G1.5
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> S5-PRY2.5 > S5-Q0.5 > 0.5 > S5-G2.5 > S5-Q1 > 1 > reset > S5-G4
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> S5-PRY5 > 1.5 > S5-Q2 > 2 > S5-PRY8 > 2.5 > 3 > S5-Q4
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> Q4 > G4 > 4 > 5 > S5-C6 > C6 > 6 > S5-C8 > 8
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> S5-C12 > 12""",
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# Goal: DR_blurprint=0, PRY_blueprint=0, tanrai_blueprint=150
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# Average time cost: 153.41706666666678
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# Average rewards: [238.69016631 238.37881965 529.71190834 528.92520834 528.39586667 150.07973333]
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'series_5_152_only_cube': """
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S5-Q0.5 > S5-DR0.5 > S5-PRY0.5 > S5-Q1 > S5-Q4 > S5-Q2 > S5-H0.5 > 0.5
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> S5-G4 > S5-G1.5 > Q1 > S5-H1 > 1 > reset > S5-DR2.5 > S5-PRY2.5
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> S5-G2.5 > 1.5 > Q2 > S5-H2 > 2 > 2.5 > 3 > S5-DR5 > S5-PRY5
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> Q4 > G4 > S5-H4 > H4 > 4 > 5 > S5-DR8 > S5-PRY8 > S5-C6
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> C6 > S5-C8 > 8 > S5-C12 > 12""",
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S5-Q0.5 > S5-DR0.5 > S5-PRY0.5 > Q0.5 > S5-Q4 > S5-Q2 > S5-Q1 > 0.5
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> S5-E-315 > S5-G1.5 > S5-G4 > Q1 > reset > S5-H1 > H1 > 1 > S5-E-031
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> S5-DR2.5 > S5-PRY2.5 > S5-G2.5 > G1.5 > 1.5 > Q2 > E2 > S5-H2 > H2
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> 2 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S5-DR5 > S5-PRY5 > Q4 > G4
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> S5-H4 > H4 > 4 > S5-C6 > DR5 > PRY5 > 5 > S5-DR8 > S5-PRY8 > S5-C8
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> C6 > 6 > S5-C12 > DR8 > PRY8 > C8 > 8 > C12 > 12
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""",
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# Goal: DR_blurprint=0, PRY_blueprint=0, tanrai_blueprint=150
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# Average time cost: 161.37177965277806
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# Average rewards: [241.92774575 241.13046242 421.82134358 421.04494941 420.46893024 150.07799978]
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'series_5_152_only': """
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S5-Q0.5 > S5-DR0.5 > S5-PRY0.5 > S5-Q4 > S5-Q1 > S5-Q2 > S5-H0.5 > 0.5
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> S5-G4 > S5-G1.5 > Q1 > 1 > S5-DR2.5 > S5-PRY2.5 > reset > S5-G2.5 > 1.5
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> Q2 > 2 > 2.5 > 3 > S5-DR5 > S5-PRY5 > Q4 > G4
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> 4 > 5 > S5-C6 > S5-DR8 > S5-PRY8 > S5-C8 > C6 > 6 > 8
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> S5-C12 > 12""",
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'series_4_blueprint_tenrai_cube': """
|
||||
S4-Q0.5 > S4-DR0.5 > S4-PRY0.5 > S4-H0.5 > S4-DR2.5 > 0.5 > S4-Q1
|
||||
> S4-H1 > S4-Q2 > reset > S4-Q4 > S4-G1.5 > Q1 > 1 > S4-H2 > S4-G4
|
||||
> 1.5 > S4-G2.5 > S4-PRY2.5 > 2.5 > S4-DR5 > Q2 > 2 > 3
|
||||
> S4-H4 > S4-DR8 > S4-PRY5 > Q4 > G4 > 4 > 5 > S4-PRY8
|
||||
> S4-C6 > C6 > 6 > S4-C8 > 8 > S4-C12 > 12""",
|
||||
'series_4_blueprint_tenrai': """
|
||||
S4-DR0.5 > S4-PRY0.5 > S4-H0.5 > S4-Q0.5 > S4-DR2.5 > 0.5 > S4-G1.5
|
||||
> S4-Q1 > S4-DR5 > S4-DR8 > S4-G4 > S4-PRY2.5 > 1 > S4-Q2 > reset
|
||||
> S4-G2.5 > S4-PRY5 > S4-PRY8 > 1.5 > 2 > S4-Q4 > 2.5 > 3
|
||||
> Q4 > G4 > 4 > 5 > S4-C6 > C6 > 6 > S4-C8 > 8
|
||||
> S4-C12 > 12""",
|
||||
'series_4_blueprint_only_cube': """
|
||||
S4-DR0.5 > S4-H0.5 > S4-PRY0.5 > S4-H1 > S4-H2 > S4-DR2.5 > S4-Q0.5
|
||||
> 0.5 > S4-DR5 > reset > S4-DR8 > S4-H4 > S4-Q1 > Q1 > 1 > S4-G1.5
|
||||
> 1.5 > S4-Q2 > Q2 > 2 > S4-G2.5 > S4-PRY2.5 > 2.5 > 3
|
||||
> S4-Q4 > S4-G4 > Q4 > G4 > 4 > S4-PRY5 > 5 > S4-PRY8 > S4-C6
|
||||
> C6 > 6 > S4-C8 > 8 > S4-C12 > 12""",
|
||||
'series_4_blueprint_only': """
|
||||
S4-DR0.5 > S4-PRY0.5 > S4-H0.5 > S4-DR8 > S4-DR2.5 > S4-DR5 > S4-G1.5
|
||||
> S4-PRY2.5 > S4-Q0.5 > 0.5 > S4-G2.5 > S4-Q1 > 1 > reset > S4-G4
|
||||
> S4-PRY5 > 1.5 > S4-Q2 > 2 > S4-PRY8 > 2.5 > 3 > S4-Q4
|
||||
> Q4 > G4 > 4 > 5 > S4-C6 > C6 > 6 > S4-C8 > 8
|
||||
> S4-C12 > 12""",
|
||||
S5-Q0.5 > S5-PRY0.5 > S5-DR0.5 > Q0.5 > S5-Q4 > S5-Q2 > S5-Q1 > 0.5
|
||||
> S5-E-315 > S5-G4 > S5-G1.5 > Q1 > 1 > S5-E-031 > S5-DR2.5 > reset
|
||||
> S5-G2.5 > S5-PRY2.5 > G1.5 > 1.5 > Q2 > E2 > 2 > DR2.5 > PRY2.5
|
||||
> G2.5 > 2.5 > S5-DR5 > S5-PRY5 > Q4 > G4 > 4 > S5-C6 > DR5 > PRY5
|
||||
> 5 > S5-DR8 > S5-PRY8 > S5-C8 > C6 > 6 > DR8 > PRY8 > C8 > 8
|
||||
> S5-C12 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=100
|
||||
# Average time cost: 124.67622465277958
|
||||
# Average rewards: [531.93022864 529.81919864 510.27473326 510.18530159 510.11215826 100.8088164]
|
||||
'series_5_blueprint_152_cube': """
|
||||
S5-DR0.5 > S5-Q0.5 > S5-PRY0.5 > 0.5 > S5-DR2.5 > S5-Q1 > S5-Q2
|
||||
> S5-H1 > S5-E-315 > S5-G1.5 > reset > S5-Q4 > S5-G4 > S5-H2 > Q1
|
||||
> H1 > 1 > S5-G2.5 > S5-DR5 > S5-PRY2.5 > G1.5 > 1.5 > S5-E-031
|
||||
> S5-DR8 > Q2 > E2 > H2 > 2 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S5-H4
|
||||
> S5-PRY5 > Q4 > G4 > H4 > 4 > S5-C6 > S5-PRY8 > DR5 > PRY5 > 5 > C6
|
||||
> 6 > S5-C8 > DR8 > PRY8 > C8 > 8 > S5-C12 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=100
|
||||
# Average time cost: 143.56399131945145
|
||||
# Average rewards: [520.06195858 519.19883191 392.86544828 392.64870495 392.49383995 102.2368499]
|
||||
'series_5_blueprint_152': """
|
||||
S5-DR0.5 > S5-PRY0.5 > S5-Q0.5 > S5-H0.5 > Q0.5 > S5-DR2.5
|
||||
> S5-G1.5 > S5-Q1 > S5-DR5 > 0.5 > S5-G4 > S5-Q2 > S5-PRY2.5 > reset
|
||||
> S5-DR8 > Q1 > 1 > S5-E-315 > S5-G2.5 > G1.5 > 1.5 > S5-E-031
|
||||
> S5-Q4 > Q2 > E2 > 2 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S5-PRY5
|
||||
> S5-PRY8 > Q4 > G4 > 4 > S5-C6 > DR5 > PRY5 > 5 > C6 > 6 > S5-C8
|
||||
> S5-C12 > DR8 > PRY8 > C8 > 8 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=0
|
||||
# Average time cost: 82.0121088194467
|
||||
# Average rewards: [519.0311752 514.64003687 653.77171198 653.72126532 653.66129615 26.97694791]
|
||||
'series_5_blueprint_only_cube': """
|
||||
S5-DR0.5 > S5-PRY0.5 > S5-H0.5 > S5-H1 > S5-H2 > S5-DR2.5 > S5-DR5
|
||||
> 0.5 > S5-DR8 > reset > S5-H4 > S5-Q1 > Q1 > H1 > 1 > S5-G1.5 > G1.5
|
||||
> 1.5 > S5-G2.5 > S5-Q2 > S5-E-315 > S5-E-031 > Q2 > E2 > H2 > 2
|
||||
> S5-PRY2.5 > S5-G4 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S5-Q4 > Q4 > G4
|
||||
> H4 > 4 > S5-PRY5 > S5-PRY8 > S5-C6 > DR5 > PRY5 > 5 > C6 > 6
|
||||
> S5-C8 > S5-C12 > DR8 > PRY8 > C8 > 8 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=0
|
||||
# Average time cost: 124.71616166666873
|
||||
# Average rewards: [514.96354877 514.70099977 355.58865468 354.96831385 354.66888635 56.48432238]
|
||||
'series_5_blueprint_only': """
|
||||
S5-DR0.5 > S5-H0.5 > S5-PRY0.5 > S5-DR8 > S5-DR5 > S5-DR2.5
|
||||
> S5-G1.5 > S5-PRY2.5 > 0.5 > S5-G2.5 > S5-G4 > reset > S5-Q1 > Q1
|
||||
> 1 > S5-PRY5 > G1.5 > 1.5 > S5-Q2 > S5-E-031 > S5-E-315 > Q2 > E2
|
||||
> 2 > S5-PRY8 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S5-Q4 > Q4 > G4 > 4
|
||||
> S5-C6 > DR5 > PRY5 > 5 > C6 > 6 > S5-C8 > DR8 > PRY8 > C8 > 8
|
||||
> S5-C12 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=0, PRY_blueprint=0, tanrai_blueprint=150
|
||||
# Average time cost: 153.41706666666678
|
||||
# Average rewards: [238.69016631 238.37881965 529.71190834 528.92520834 528.39586667 150.07973333]
|
||||
'series_4_tenrai_only_cube': """
|
||||
S4-Q0.5 > S4-DR0.5 > S4-PRY0.5 > S4-Q1 > S4-Q4 > S4-Q2 > S4-H0.5 > 0.5
|
||||
> S4-G4 > S4-G1.5 > Q1 > S4-H1 > 1 > reset > S4-DR2.5 > S4-PRY2.5
|
||||
> S4-G2.5 > 1.5 > Q2 > S4-H2 > 2 > 2.5 > 3 > S4-DR5 > S4-PRY5
|
||||
> Q4 > G4 > S4-H4 > H4 > 4 > 5 > S4-DR8 > S4-PRY8 > S4-C6
|
||||
> C6 > S4-C8 > 8 > S4-C12 > 12""",
|
||||
S4-Q0.5 > S4-DR0.5 > S4-PRY0.5 > Q0.5 > S4-Q4 > S4-Q2 > S4-Q1 > 0.5
|
||||
> S4-E-315 > S4-G1.5 > S4-G4 > Q1 > reset > S4-H1 > H1 > 1 > S4-E-031
|
||||
> S4-DR2.5 > S4-PRY2.5 > S4-G2.5 > G1.5 > 1.5 > Q2 > E2 > S4-H2 > H2
|
||||
> 2 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S4-DR5 > S4-PRY5 > Q4 > G4
|
||||
> S4-H4 > H4 > 4 > S4-C6 > DR5 > PRY5 > 5 > S4-DR8 > S4-PRY8 > S4-C8
|
||||
> C6 > 6 > S4-C12 > DR8 > PRY8 > C8 > 8 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=0, PRY_blueprint=0, tanrai_blueprint=150
|
||||
# Average time cost: 161.37177965277806
|
||||
# Average rewards: [241.92774575 241.13046242 421.82134358 421.04494941 420.46893024 150.07799978]
|
||||
'series_4_tenrai_only': """
|
||||
S4-Q0.5 > S4-DR0.5 > S4-PRY0.5 > S4-Q4 > S4-Q1 > S4-Q2 > S4-H0.5 > 0.5
|
||||
> S4-G4 > S4-G1.5 > Q1 > 1 > S4-DR2.5 > S4-PRY2.5 > reset > S4-G2.5 > 1.5
|
||||
> Q2 > 2 > 2.5 > 3 > S4-DR5 > S4-PRY5 > Q4 > G4
|
||||
> 4 > 5 > S4-C6 > S4-DR8 > S4-PRY8 > S4-C8 > C6 > 6 > 8
|
||||
> S4-C12 > 12""",
|
||||
'series_2_than_3_457_234': """
|
||||
S2-Q0.5 > S2-PRY0.5 > S2-DR0.5 > S2-Q4 > S2-Q1 > S2-Q2 > S2-H0.5 > 0.5
|
||||
> S3-Q1 > S3-Q2 > S2-G4 > S3-Q4 > S2-G1.5 > S2-DR2.5 > reset > Q1 > S2-PRY2.5 > S2-G2.5 > H1 > 1.5
|
||||
> Q2 > 2.5 > S2-DR5 > S2-PRY5 > Q4 > G4 > 5 > H2 > S2-C6 > S2-DR8 > S2-PRY8 > S2-C8
|
||||
> 6 > 8 > 4 > S2-C12 > 12""",
|
||||
S4-Q0.5 > S4-PRY0.5 > S4-DR0.5 > Q0.5 > S4-Q4 > S4-Q2 > S4-Q1 > 0.5
|
||||
> S4-E-315 > S4-G4 > S4-G1.5 > Q1 > 1 > S4-E-031 > S4-DR2.5 > reset
|
||||
> S4-G2.5 > S4-PRY2.5 > G1.5 > 1.5 > Q2 > E2 > 2 > DR2.5 > PRY2.5
|
||||
> G2.5 > 2.5 > S4-DR5 > S4-PRY5 > Q4 > G4 > 4 > S4-C6 > DR5 > PRY5
|
||||
> 5 > S4-DR8 > S4-PRY8 > S4-C8 > C6 > 6 > DR8 > PRY8 > C8 > 8
|
||||
> S4-C12 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=100
|
||||
# Average time cost: 124.67622465277958
|
||||
# Average rewards: [531.93022864 529.81919864 510.27473326 510.18530159 510.11215826 100.8088164]
|
||||
'series_4_blueprint_tenrai_cube': """
|
||||
S4-DR0.5 > S4-Q0.5 > S4-PRY0.5 > 0.5 > S4-DR2.5 > S4-Q1 > S4-Q2
|
||||
> S4-H1 > S4-E-315 > S4-G1.5 > reset > S4-Q4 > S4-G4 > S4-H2 > Q1
|
||||
> H1 > 1 > S4-G2.5 > S4-DR5 > S4-PRY2.5 > G1.5 > 1.5 > S4-E-031
|
||||
> S4-DR8 > Q2 > E2 > H2 > 2 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S4-H4
|
||||
> S4-PRY5 > Q4 > G4 > H4 > 4 > S4-C6 > S4-PRY8 > DR5 > PRY5 > 5 > C6
|
||||
> 6 > S4-C8 > DR8 > PRY8 > C8 > 8 > S4-C12 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=100
|
||||
# Average time cost: 143.56399131945145
|
||||
# Average rewards: [520.06195858 519.19883191 392.86544828 392.64870495 392.49383995 102.2368499]
|
||||
'series_4_blueprint_tenrai': """
|
||||
S4-DR0.5 > S4-PRY0.5 > S4-Q0.5 > S4-H0.5 > Q0.5 > S4-DR2.5
|
||||
> S4-G1.5 > S4-Q1 > S4-DR5 > 0.5 > S4-G4 > S4-Q2 > S4-PRY2.5 > reset
|
||||
> S4-DR8 > Q1 > 1 > S4-E-315 > S4-G2.5 > G1.5 > 1.5 > S4-E-031
|
||||
> S4-Q4 > Q2 > E2 > 2 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S4-PRY5
|
||||
> S4-PRY8 > Q4 > G4 > 4 > S4-C6 > DR5 > PRY5 > 5 > C6 > 6 > S4-C8
|
||||
> S4-C12 > DR8 > PRY8 > C8 > 8 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=0
|
||||
# Average time cost: 82.0121088194467
|
||||
# Average rewards: [519.0311752 514.64003687 653.77171198 653.72126532 653.66129615 26.97694791]
|
||||
'series_4_blueprint_only_cube': """
|
||||
S4-DR0.5 > S4-PRY0.5 > S4-H0.5 > S4-H1 > S4-H2 > S4-DR2.5 > S4-DR5
|
||||
> 0.5 > S4-DR8 > reset > S4-H4 > S4-Q1 > Q1 > H1 > 1 > S4-G1.5 > G1.5
|
||||
> 1.5 > S4-G2.5 > S4-Q2 > S4-E-315 > S4-E-031 > Q2 > E2 > H2 > 2
|
||||
> S4-PRY2.5 > S4-G4 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S4-Q4 > Q4 > G4
|
||||
> H4 > 4 > S4-PRY5 > S4-PRY8 > S4-C6 > DR5 > PRY5 > 5 > C6 > 6
|
||||
> S4-C8 > S4-C12 > DR8 > PRY8 > C8 > 8 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=0
|
||||
# Average time cost: 124.71616166666873
|
||||
# Average rewards: [514.96354877 514.70099977 355.58865468 354.96831385 354.66888635 56.48432238]
|
||||
'series_4_blueprint_only': """
|
||||
S4-DR0.5 > S4-H0.5 > S4-PRY0.5 > S4-DR8 > S4-DR5 > S4-DR2.5
|
||||
> S4-G1.5 > S4-PRY2.5 > 0.5 > S4-G2.5 > S4-G4 > reset > S4-Q1 > Q1
|
||||
> 1 > S4-PRY5 > G1.5 > 1.5 > S4-Q2 > S4-E-031 > S4-E-315 > Q2 > E2
|
||||
> 2 > S4-PRY8 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S4-Q4 > Q4 > G4 > 4
|
||||
> S4-C6 > DR5 > PRY5 > 5 > C6 > 6 > S4-C8 > DR8 > PRY8 > C8 > 8
|
||||
> S4-C12 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=0, PRY_blueprint=0, tanrai_blueprint=150
|
||||
# Average time cost: 153.41706666666678
|
||||
# Average rewards: [238.69016631 238.37881965 529.71190834 528.92520834 528.39586667 150.07973333]
|
||||
'series_3_234_only_cube': """
|
||||
S3-Q0.5 > S3-DR0.5 > S3-PRY0.5 > Q0.5 > S3-Q4 > S3-Q2 > S3-Q1 > 0.5
|
||||
> S3-E-315 > S3-G1.5 > S3-G4 > Q1 > reset > S3-H1 > H1 > 1 > S3-E-031
|
||||
> S3-DR2.5 > S3-PRY2.5 > S3-G2.5 > G1.5 > 1.5 > Q2 > E2 > S3-H2 > H2
|
||||
> 2 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S3-DR5 > S3-PRY5 > Q4 > G4
|
||||
> S3-H4 > H4 > 4 > S3-C6 > DR5 > PRY5 > 5 > S3-DR8 > S3-PRY8 > S3-C8
|
||||
> C6 > 6 > S3-C12 > DR8 > PRY8 > C8 > 8 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=0, PRY_blueprint=0, tanrai_blueprint=150
|
||||
# Average time cost: 161.37177965277806
|
||||
# Average rewards: [241.92774575 241.13046242 421.82134358 421.04494941 420.46893024 150.07799978]
|
||||
'series_3_234_only': """
|
||||
S2-Q0.5 > S2-PRY0.5 > S2-DR0.5 > S3-Q4 > S3-Q1 > S3-Q2 > S2-H0.5 > 0.5
|
||||
> S3-G4 > S3-G1.5 > S3-DR2.5 > reset > Q1 > S3-PRY2.5 > S3-G2.5 > H1 > 1.5
|
||||
> Q2 > 2.5 > S3-DR5 > S3-PRY5 > Q4 > G4 > 5 > H2 > S2-C6 > S3-DR8 > S3-PRY8 > S3-C8
|
||||
> 6 > 8 > 4 > S3-C12 > 12""",
|
||||
S3-Q0.5 > S3-PRY0.5 > S3-DR0.5 > Q0.5 > S3-Q4 > S3-Q2 > S3-Q1 > 0.5
|
||||
> S3-E-315 > S3-G4 > S3-G1.5 > Q1 > 1 > S3-E-031 > S3-DR2.5 > reset
|
||||
> S3-G2.5 > S3-PRY2.5 > G1.5 > 1.5 > Q2 > E2 > 2 > DR2.5 > PRY2.5
|
||||
> G2.5 > 2.5 > S3-DR5 > S3-PRY5 > Q4 > G4 > 4 > S3-C6 > DR5 > PRY5
|
||||
> 5 > S3-DR8 > S3-PRY8 > S3-C8 > C6 > 6 > DR8 > PRY8 > C8 > 8
|
||||
> S3-C12 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=100
|
||||
# Average time cost: 124.67622465277958
|
||||
# Average rewards: [531.93022864 529.81919864 510.27473326 510.18530159 510.11215826 100.8088164]
|
||||
'series_3_blueprint_234_cube': """
|
||||
S3-DR0.5 > S3-Q0.5 > S3-PRY0.5 > 0.5 > S3-DR2.5 > S3-Q1 > S3-Q2
|
||||
> S3-H1 > S3-E-315 > S3-G1.5 > reset > S3-Q4 > S3-G4 > S3-H2 > Q1
|
||||
> H1 > 1 > S3-G2.5 > S3-DR5 > S3-PRY2.5 > G1.5 > 1.5 > S3-E-031
|
||||
> S3-DR8 > Q2 > E2 > H2 > 2 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S3-H4
|
||||
> S3-PRY5 > Q4 > G4 > H4 > 4 > S3-C6 > S3-PRY8 > DR5 > PRY5 > 5 > C6
|
||||
> 6 > S3-C8 > DR8 > PRY8 > C8 > 8 > S3-C12 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=100
|
||||
# Average time cost: 143.56399131945145
|
||||
# Average rewards: [520.06195858 519.19883191 392.86544828 392.64870495 392.49383995 102.2368499]
|
||||
'series_3_blueprint_234': """
|
||||
S3-Q0.5 > S3-DR0.5 > S3-PRY0.5 > S3-H0.5 > S3-DR2.5 > 0.5 > S3-G1.5
|
||||
> S3-Q1 > S3-G4 > S3-DR5 > S3-DR8 > S3-PRY2.5 > 1 > S3-Q2 > reset
|
||||
> S3-G2.5 > S3-PRY5 > S3-PRY8 > 1.5 > 2 > S3-Q4 > 2.5 > 4 > 5 > S3-C6
|
||||
> S3-C8 > 6 > 8 > S3-C12 > 12""",
|
||||
'series_2_blueprint_457': """
|
||||
S2-Q0.5 > S2-DR0.5 > S2-PRY0.5 > S2-H0.5 > S2-DR2.5 > 0.5 > S2-G1.5
|
||||
> S2-Q1 > S2-G4 > S2-DR5 > S2-DR8 > S2-PRY2.5 > 1 > S2-Q2 > reset
|
||||
> S2-G2.5 > S2-PRY5 > S2-PRY8 > 1.5 > 2 > S2-Q4 > 2.5 > 4 > 5 > S2-C6
|
||||
> S2-C8 > 6 > 8 > S2-C12 > 12""",
|
||||
S3-DR0.5 > S3-PRY0.5 > S3-Q0.5 > S3-H0.5 > Q0.5 > S3-DR2.5
|
||||
> S3-G1.5 > S3-Q1 > S3-DR5 > 0.5 > S3-G4 > S3-Q2 > S3-PRY2.5 > reset
|
||||
> S3-DR8 > Q1 > 1 > S3-E-315 > S3-G2.5 > G1.5 > 1.5 > S3-E-031
|
||||
> S3-Q4 > Q2 > E2 > 2 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S3-PRY5
|
||||
> S3-PRY8 > Q4 > G4 > 4 > S3-C6 > DR5 > PRY5 > 5 > C6 > 6 > S3-C8
|
||||
> S3-C12 > DR8 > PRY8 > C8 > 8 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=0
|
||||
# Average time cost: 82.0121088194467
|
||||
# Average rewards: [519.0311752 514.64003687 653.77171198 653.72126532 653.66129615 26.97694791]
|
||||
'series_3_blueprint_only_cube': """
|
||||
S3-DR0.5 > S3-PRY0.5 > S3-H0.5 > S3-H1 > S3-H2 > S3-DR2.5 > S3-DR5
|
||||
> 0.5 > S3-DR8 > reset > S3-H4 > S3-Q1 > Q1 > H1 > 1 > S3-G1.5 > G1.5
|
||||
> 1.5 > S3-G2.5 > S3-Q2 > S3-E-315 > S3-E-031 > Q2 > E2 > H2 > 2
|
||||
> S3-PRY2.5 > S3-G4 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S3-Q4 > Q4 > G4
|
||||
> H4 > 4 > S3-PRY5 > S3-PRY8 > S3-C6 > DR5 > PRY5 > 5 > C6 > 6
|
||||
> S3-C8 > S3-C12 > DR8 > PRY8 > C8 > 8 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=0
|
||||
# Average time cost: 124.71616166666873
|
||||
# Average rewards: [514.96354877 514.70099977 355.58865468 354.96831385 354.66888635 56.48432238]
|
||||
'series_3_blueprint_only': """
|
||||
S3-DR0.5 > S3-PRY0.5 > S3-H0.5 > S3-Q0.5 > S3-DR2.5 > S3-G4 > S3-G1.5
|
||||
> S3-PRY2.5 > 0.5 > S3-G2.5 > S3-Q1 > 1 > reset > S3-DR5 > S3-DR8
|
||||
> S3-PRY5 > 1.5 > S3-Q2 > 2 > S3-PRY8 > 2.5 > S3-Q4 > 4 > 5 > S3-C6
|
||||
> 6 > S3-C8 > 8 > S3-C12 > 12""",
|
||||
'series_2_blueprint_only': """
|
||||
S2-DR0.5 > S2-PRY0.5 > S2-H0.5 > S2-Q0.5 > S2-DR2.5 > S2-G4 > S2-G1.5
|
||||
> S2-PRY2.5 > 0.5 > S2-G2.5 > S2-Q1 > 1 > reset > S2-DR5 > S2-DR8
|
||||
> S2-PRY5 > 1.5 > S2-Q2 > 2 > S2-PRY8 > 2.5 > S2-Q4 > 4 > 5 > S2-C6
|
||||
> 6 > S2-C8 > 8 > S2-C12 > 12""",
|
||||
S3-DR0.5 > S3-H0.5 > S3-PRY0.5 > S3-DR8 > S3-DR5 > S3-DR2.5
|
||||
> S3-G1.5 > S3-PRY2.5 > 0.5 > S3-G2.5 > S3-G4 > reset > S3-Q1 > Q1
|
||||
> 1 > S3-PRY5 > G1.5 > 1.5 > S3-Q2 > S3-E-031 > S3-E-315 > Q2 > E2
|
||||
> 2 > S3-PRY8 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S3-Q4 > Q4 > G4 > 4
|
||||
> S3-C6 > DR5 > PRY5 > 5 > C6 > 6 > S3-C8 > DR8 > PRY8 > C8 > 8
|
||||
> S3-C12 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=0, PRY_blueprint=0, tanrai_blueprint=150
|
||||
# Average time cost: 153.41706666666678
|
||||
# Average rewards: [238.69016631 238.37881965 529.71190834 528.92520834 528.39586667 150.07973333]
|
||||
'series_2_457_only_cube': """
|
||||
S2-Q0.5 > S2-DR0.5 > S2-PRY0.5 > Q0.5 > S2-Q4 > S2-Q2 > S2-Q1 > 0.5
|
||||
> S2-E-315 > S2-G1.5 > S2-G4 > Q1 > reset > S2-H1 > H1 > 1 > S2-E-031
|
||||
> S2-DR2.5 > S2-PRY2.5 > S2-G2.5 > G1.5 > 1.5 > Q2 > E2 > S2-H2 > H2
|
||||
> 2 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S2-DR5 > S2-PRY5 > Q4 > G4
|
||||
> S2-H4 > H4 > 4 > S2-C6 > DR5 > PRY5 > 5 > S2-DR8 > S2-PRY8 > S2-C8
|
||||
> C6 > 6 > S2-C12 > DR8 > PRY8 > C8 > 8 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=0, PRY_blueprint=0, tanrai_blueprint=150
|
||||
# Average time cost: 161.37177965277806
|
||||
# Average rewards: [241.92774575 241.13046242 421.82134358 421.04494941 420.46893024 150.07799978]
|
||||
'series_2_457_only': """
|
||||
S2-Q0.5 > S2-PRY0.5 > S2-DR0.5 > S2-Q4 > S2-Q1 > S2-Q2 > S2-H0.5 > 0.5
|
||||
> Q1 > S2-G4 > S2-G1.5 > S2-DR2.5 > reset > S2-PRY2.5 > S2-G2.5 > H1 > 1.5
|
||||
> Q2 > 2.5 > S2-DR5 > S2-PRY5 > Q4 > G4 > 5 > H2 > S2-C6 > S2-DR8 > S2-PRY8 > S2-C8
|
||||
> 6 > 8 > 4 > S2-C12 > 12"""
|
||||
S2-Q0.5 > S2-PRY0.5 > S2-DR0.5 > Q0.5 > S2-Q4 > S2-Q2 > S2-Q1 > 0.5
|
||||
> S2-E-315 > S2-G4 > S2-G1.5 > Q1 > 1 > S2-E-031 > S2-DR2.5 > reset
|
||||
> S2-G2.5 > S2-PRY2.5 > G1.5 > 1.5 > Q2 > E2 > 2 > DR2.5 > PRY2.5
|
||||
> G2.5 > 2.5 > S2-DR5 > S2-PRY5 > Q4 > G4 > 4 > S2-C6 > DR5 > PRY5
|
||||
> 5 > S2-DR8 > S2-PRY8 > S2-C8 > C6 > 6 > DR8 > PRY8 > C8 > 8
|
||||
> S2-C12 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=100
|
||||
# Average time cost: 124.67622465277958
|
||||
# Average rewards: [531.93022864 529.81919864 510.27473326 510.18530159 510.11215826 100.8088164]
|
||||
'series_2_blueprint_457_cube': """
|
||||
S2-DR0.5 > S2-Q0.5 > S2-PRY0.5 > 0.5 > S2-DR2.5 > S2-Q1 > S2-Q2
|
||||
> S2-H1 > S2-E-315 > S2-G1.5 > reset > S2-Q4 > S2-G4 > S2-H2 > Q1
|
||||
> H1 > 1 > S2-G2.5 > S2-DR5 > S2-PRY2.5 > G1.5 > 1.5 > S2-E-031
|
||||
> S2-DR8 > Q2 > E2 > H2 > 2 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S2-H4
|
||||
> S2-PRY5 > Q4 > G4 > H4 > 4 > S2-C6 > S2-PRY8 > DR5 > PRY5 > 5 > C6
|
||||
> 6 > S2-C8 > DR8 > PRY8 > C8 > 8 > S2-C12 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=100
|
||||
# Average time cost: 143.56399131945145
|
||||
# Average rewards: [520.06195858 519.19883191 392.86544828 392.64870495 392.49383995 102.2368499]
|
||||
'series_2_blueprint_457': """
|
||||
S2-DR0.5 > S2-PRY0.5 > S2-Q0.5 > S2-H0.5 > Q0.5 > S2-DR2.5
|
||||
> S2-G1.5 > S2-Q1 > S2-DR5 > 0.5 > S2-G4 > S2-Q2 > S2-PRY2.5 > reset
|
||||
> S2-DR8 > Q1 > 1 > S2-E-315 > S2-G2.5 > G1.5 > 1.5 > S2-E-031
|
||||
> S2-Q4 > Q2 > E2 > 2 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S2-PRY5
|
||||
> S2-PRY8 > Q4 > G4 > 4 > S2-C6 > DR5 > PRY5 > 5 > C6 > 6 > S2-C8
|
||||
> S2-C12 > DR8 > PRY8 > C8 > 8 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=0
|
||||
# Average time cost: 82.0121088194467
|
||||
# Average rewards: [519.0311752 514.64003687 653.77171198 653.72126532 653.66129615 26.97694791]
|
||||
'series_2_blueprint_only_cube': """
|
||||
S2-DR0.5 > S2-PRY0.5 > S2-H0.5 > S2-H1 > S2-H2 > S2-DR2.5 > S2-DR5
|
||||
> 0.5 > S2-DR8 > reset > S2-H4 > S2-Q1 > Q1 > H1 > 1 > S2-G1.5 > G1.5
|
||||
> 1.5 > S2-G2.5 > S2-Q2 > S2-E-315 > S2-E-031 > Q2 > E2 > H2 > 2
|
||||
> S2-PRY2.5 > S2-G4 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S2-Q4 > Q4 > G4
|
||||
> H4 > 4 > S2-PRY5 > S2-PRY8 > S2-C6 > DR5 > PRY5 > 5 > C6 > 6
|
||||
> S2-C8 > S2-C12 > DR8 > PRY8 > C8 > 8 > C12 > 12
|
||||
""",
|
||||
# Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=0
|
||||
# Average time cost: 124.71616166666873
|
||||
# Average rewards: [514.96354877 514.70099977 355.58865468 354.96831385 354.66888635 56.48432238]
|
||||
'series_2_blueprint_only': """
|
||||
S2-DR0.5 > S2-H0.5 > S2-PRY0.5 > S2-DR8 > S2-DR5 > S2-DR2.5
|
||||
> S2-G1.5 > S2-PRY2.5 > 0.5 > S2-G2.5 > S2-G4 > reset > S2-Q1 > Q1
|
||||
> 1 > S2-PRY5 > G1.5 > 1.5 > S2-Q2 > S2-E-031 > S2-E-315 > Q2 > E2
|
||||
> 2 > S2-PRY8 > DR2.5 > PRY2.5 > G2.5 > 2.5 > S2-Q4 > Q4 > G4 > 4
|
||||
> S2-C6 > DR5 > PRY5 > 5 > C6 > 6 > S2-C8 > DR8 > PRY8 > C8 > 8
|
||||
> S2-C12 > C12 > 12
|
||||
""",
|
||||
# Old community filters
|
||||
'series_2_than_3_457_234': """
|
||||
S2-Q0.5 > S2-PRY0.5 > S2-DR0.5 > S2-Q4 > S2-Q1 > S2-Q2 > S2-H0.5
|
||||
> 0.5 > S3-Q1 > S3-Q2 > S2-G4 > S3-Q4 > S2-G1.5 > S2-DR2.5 > reset
|
||||
> Q1 > S2-PRY2.5 > S2-G2.5 > H1 > 1.5 > Q2 > 2.5 > S2-DR5 > S2-PRY5
|
||||
> Q4 > G4 > 5 > H2 > S2-C6 > S2-DR8 > S2-PRY8 > S2-C8 > 6 > 8 > 4
|
||||
> S2-C12 > 12
|
||||
""",
|
||||
}
|
||||
|
||||
195
module/research/preset_generator.py
Normal file
195
module/research/preset_generator.py
Normal file
@ -0,0 +1,195 @@
|
||||
import re
|
||||
|
||||
|
||||
def split_filter(string):
|
||||
if isinstance(string, list):
|
||||
return string
|
||||
return [f.strip(' \t\r\n') for f in string.split('>')]
|
||||
|
||||
|
||||
def join_filter(selection):
|
||||
if isinstance(selection, str):
|
||||
return selection
|
||||
return ' > '.join(selection)
|
||||
|
||||
|
||||
def beautify_filter(list_filter):
|
||||
if isinstance(list_filter, str):
|
||||
list_filter = split_filter(list_filter)
|
||||
|
||||
out = []
|
||||
length = 0
|
||||
for selection in list_filter:
|
||||
if length + len(selection) + 3 > 70:
|
||||
out.append('\n')
|
||||
length = 0
|
||||
out.append(selection)
|
||||
length += len(selection) + 3
|
||||
string = ' > '.join(out).strip('\n >').replace(' > \n', '\n').replace('\n ', '\n')
|
||||
return string
|
||||
|
||||
|
||||
def translate(string: str, target='series_4_tenrai_only_cube', for_simulate=False):
|
||||
res = re.search(r'series_?(\d)', target)
|
||||
if res:
|
||||
series = res.group(1)
|
||||
else:
|
||||
print(f'Translate target from unknown series: {target}')
|
||||
return
|
||||
cube = 'cube' in target
|
||||
string = string.replace('S4-H0.5 > !4-0.5', '0.5')
|
||||
string = string.replace('!4-0.5', '0.5')
|
||||
# Add Q0.5 after the last 0.5 selection
|
||||
selections = split_filter(string)
|
||||
last_05 = 0
|
||||
for index, sele in enumerate(selections):
|
||||
if sele == '0.5':
|
||||
break
|
||||
if '0.5' in sele:
|
||||
last_05 = index
|
||||
if last_05:
|
||||
selections.insert(last_05 + 1, 'Q0.5')
|
||||
string = join_filter(selections)
|
||||
string = string.replace('S4-Q0.5 > Q0.5 > 0.5', '0.5')
|
||||
string = string.replace('Q0.5 > 0.5', '0.5')
|
||||
|
||||
string = string.replace('!4-1.5', 'G1.5 > 1.5')
|
||||
string = string.replace('!4-1', 'Q1 > H1 > 1')
|
||||
string = string.replace('!4-2.5', 'DR2.5 > PRY2.5 > G2.5 > 2.5')
|
||||
string = string.replace('!4-2', 'Q2 > E2 > H2 > 2')
|
||||
string = string.replace('!4-4', 'Q4 > G4 > H4 > 4')
|
||||
string = string.replace('!4-5', 'DR5 > PRY5 > 5')
|
||||
string = string.replace('!4-C6', 'C6 > 6')
|
||||
string = string.replace('!4-C8', 'DR8 > PRY8 > C8 > 8')
|
||||
string = string.replace('!4-C12', 'C12 > 12')
|
||||
|
||||
if not for_simulate:
|
||||
string = string.replace('A2', 'E-315')
|
||||
string = string.replace('Z2', 'E-031')
|
||||
|
||||
if not cube:
|
||||
string = re.sub(r'(S4-)?H[124] > ', '', string)
|
||||
string = string.replace('H1 > 1 > reset > S4-H1', 'reset > S4-H1 > H1 > 1')
|
||||
string = string.replace('H1 > 1 > S4-H1', 'S4-H1 > H1 > 1')
|
||||
string = string.replace('H2 > 2 > S4-H2', 'S4-H2 > H2 > 2')
|
||||
string = string.replace('H4 > 4 > S4-H4', 'S4-H4 > H4 > 4')
|
||||
string = re.sub(r'S4', f'S{series}', string)
|
||||
|
||||
return beautify_filter(string)
|
||||
|
||||
|
||||
def convert_name(name, series):
|
||||
name = re.sub(r'series_\d', f'series_{series}', name)
|
||||
if 'series_5' in name:
|
||||
name = name.replace('tenrai', '152')
|
||||
if 'series_4' in name:
|
||||
pass
|
||||
if 'series_3' in name:
|
||||
name = name.replace('tenrai', '234')
|
||||
if 'series_2' in name:
|
||||
name = name.replace('tenrai', '457')
|
||||
return name
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
from module.config.code_generator import *
|
||||
|
||||
Value(FILTER_STRING_SHORTEST='0.5 > 1 > 1.5 > 2 > 2.5 > 3 > 4 > 5 > 6 > 8 > 10 > 12')
|
||||
Value(
|
||||
FILTER_STRING_CHEAPEST='Q1 > Q2 > T3 > T4 > Q4 > C6 > T6 > C8 > C12 > G1.5 > D2.5 > G2.5 > D5 > Q0.5 > G4 > D8 > H1 > H2 > H0.5 > D0.5 > H4')
|
||||
with Dict('DICT_FILTER_PRESET'):
|
||||
for series in [5, 4, 3, 2]:
|
||||
def new_filter(**kwargs):
|
||||
for k, v in kwargs.items():
|
||||
k = convert_name(k, series)
|
||||
v = translate(v, target=k)
|
||||
DictItem(k, v)
|
||||
|
||||
# 1
|
||||
Comment("""
|
||||
Goal: DR_blurprint=0, PRY_blueprint=0, tanrai_blueprint=150
|
||||
Average time cost: 153.41706666666678
|
||||
Average rewards: [238.69016631 238.37881965 529.71190834 528.92520834 528.39586667 150.07973333]
|
||||
""")
|
||||
new_filter(series_4_tenrai_only_cube="""
|
||||
S4-Q0.5 > S4-DR0.5 > S4-PRY0.5 > S4-Q4 > S4-Q2 > S4-Q1 > !4-0.5
|
||||
> S4-A2 > S4-G1.5 > S4-G4 > !4-1 > reset > S4-H1 > S4-Z2
|
||||
> S4-DR2.5 > S4-PRY2.5 > S4-G2.5 > !4-1.5 > !4-2 > S4-H2 > !4-2.5 > S4-DR5 > S4-PRY5
|
||||
> !4-4 > S4-H4 > S4-C6 > !4-5 > S4-DR8 > S4-PRY8 > S4-C8 > !4-C6 > S4-C12
|
||||
> !4-C8 > !4-C12
|
||||
""")
|
||||
# 2
|
||||
Comment("""
|
||||
Goal: DR_blurprint=0, PRY_blueprint=0, tanrai_blueprint=150
|
||||
Average time cost: 161.37177965277806
|
||||
Average rewards: [241.92774575 241.13046242 421.82134358 421.04494941 420.46893024 150.07799978]
|
||||
""")
|
||||
new_filter(series_4_tenrai_only="""
|
||||
S4-Q0.5 > S4-PRY0.5 > S4-DR0.5 > S4-Q4 > S4-Q2 > S4-Q1 > S4-H0.5 > !4-0.5 > S4-A2
|
||||
> S4-G4 > S4-H1 > S4-G1.5 > !4-1 > S4-Z2 > S4-DR2.5 > reset
|
||||
> S4-G2.5 > S4-PRY2.5 > !4-1.5 > !4-2 > !4-2.5 > S4-H2 > S4-H4 > S4-DR5
|
||||
> S4-PRY5 > !4-4 > S4-C6 > !4-5 > S4-DR8 > S4-PRY8 > S4-C8 > !4-C6
|
||||
> !4-C8 > S4-C12 > !4-C12
|
||||
""")
|
||||
# 5
|
||||
Comment("""
|
||||
Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=100
|
||||
Average time cost: 124.67622465277958
|
||||
Average rewards: [531.93022864 529.81919864 510.27473326 510.18530159 510.11215826 100.8088164]
|
||||
""")
|
||||
new_filter(series_4_blueprint_tenrai_cube="""
|
||||
S4-DR0.5 > S4-Q0.5 > S4-PRY0.5 > S4-H0.5 > !4-0.5 > S4-DR2.5 > S4-Q1
|
||||
> S4-Q2 > S4-H1 > S4-A2 > S4-G1.5 > reset > S4-Q4 > S4-G4 > S4-H2
|
||||
> !4-1 > S4-G2.5 > S4-DR5 > S4-PRY2.5 > !4-1.5 > S4-Z2 > S4-DR8
|
||||
> !4-2 > !4-2.5 > S4-H4 > S4-PRY5 > !4-4 > S4-C6 > S4-PRY8 > !4-5 > !4-C6 > S4-C8
|
||||
> !4-C8 > S4-C12 > !4-C12
|
||||
""")
|
||||
# 6
|
||||
Comment("""
|
||||
Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=100
|
||||
Average time cost: 143.56399131945145
|
||||
Average rewards: [520.06195858 519.19883191 392.86544828 392.64870495 392.49383995 102.2368499]
|
||||
""")
|
||||
new_filter(series_4_blueprint_tenrai="""
|
||||
S4-DR0.5 > S4-PRY0.5 > S4-Q0.5 > S4-H1 > S4-H0.5 > S4-DR2.5 > S4-G1.5
|
||||
> S4-Q1 > S4-DR5 > !4-0.5 > S4-G4 > S4-Q2 > S4-PRY2.5 > reset > S4-DR8
|
||||
> !4-1 > S4-A2 > S4-G2.5 > S4-H2 > !4-1.5 > S4-Z2 > S4-H4
|
||||
> S4-Q4 > !4-2 > !4-2.5 > S4-PRY5 > S4-PRY8 > !4-4 > S4-C6 > !4-5 > !4-C6 > S4-C8
|
||||
> S4-C12 > !4-C8 > !4-C12
|
||||
""")
|
||||
# 3
|
||||
Comment("""
|
||||
Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=0
|
||||
Average time cost: 82.0121088194467
|
||||
Average rewards: [519.0311752 514.64003687 653.77171198 653.72126532 653.66129615 26.97694791]
|
||||
""")
|
||||
new_filter(series_4_blueprint_only_cube="""
|
||||
S4-DR0.5 > S4-PRY0.5 > S4-H0.5 > S4-H1 > S4-H2 > S4-DR2.5 > S4-DR5 > S4-Q0.5
|
||||
> !4-0.5 > S4-DR8 > reset > S4-H4 > S4-Q1 > !4-1 > S4-G1.5 > !4-1.5
|
||||
> S4-G2.5 > S4-Q2 > S4-A2 > S4-Z2 > !4-2 > S4-PRY2.5 > S4-G4 > !4-2.5
|
||||
> S4-Q4 > !4-4 > S4-PRY5 > S4-PRY8 > S4-C6 > !4-5 > !4-C6 > S4-C8
|
||||
> S4-C12 > !4-C8 > !4-C12
|
||||
""")
|
||||
# 4
|
||||
Comment("""
|
||||
Goal: DR_blurprint=513, PRY_blueprint=343, tanrai_blueprint=0
|
||||
Average time cost: 124.71616166666873
|
||||
Average rewards: [514.96354877 514.70099977 355.58865468 354.96831385 354.66888635 56.48432238]
|
||||
""")
|
||||
new_filter(series_4_blueprint_only="""
|
||||
S4-DR0.5 > S4-H0.5 > S4-PRY0.5 > S4-DR8 > S4-DR5
|
||||
> S4-DR2.5 > S4-G1.5 > S4-PRY2.5 > S4-Q0.5 > !4-0.5 > S4-G2.5 > S4-G4
|
||||
> reset > S4-Q1 > !4-1 > S4-PRY5 > !4-1.5 > S4-Q2 > S4-Z2 > S4-A2 > !4-2 > S4-PRY8
|
||||
> !4-2.5 > S4-Q4 > !4-4 > S4-C6 > !4-5 > !4-C6 > S4-C8
|
||||
> !4-C8 > S4-C12 > !4-C12
|
||||
""")
|
||||
|
||||
Comment('Old community filters')
|
||||
DictItem(series_2_than_3_457_234=beautify_filter("""
|
||||
S2-Q0.5 > S2-PRY0.5 > S2-DR0.5 > S2-Q4 > S2-Q1 > S2-Q2 > S2-H0.5 > 0.5
|
||||
> S3-Q1 > S3-Q2 > S2-G4 > S3-Q4 > S2-G1.5 > S2-DR2.5 > reset > Q1 > S2-PRY2.5 > S2-G2.5 > H1 > 1.5
|
||||
> Q2 > 2.5 > S2-DR5 > S2-PRY5 > Q4 > G4 > 5 > H2 > S2-C6 > S2-DR8 > S2-PRY8 > S2-C8
|
||||
> 6 > 8 > 4 > S2-C12 > 12
|
||||
"""))
|
||||
from module.logger import logger
|
||||
generator.write('./module/research/preset.py')
|
||||
Loading…
Reference in New Issue
Block a user