From 733b009f627f8e5c81c3e5461391d3aa3e0dd18f Mon Sep 17 00:00:00 2001
From: ysymyth <shunyuyao.cs@gmail.com>
Date: Mon, 3 Jul 2023 22:16:03 -0400
Subject: [PATCH 1/5] tot package

---
 MANIFEST.in                                   |    4 +
 fake.png => pics/fake.png                     |  Bin
 teaser.png => pics/teaser.png                 |  Bin
 pyproject.toml                                |   35 +
 readme.md                                     |    4 +-
 requirements.txt                              |    1 +
 run.py                                        |  105 +-
 scripts/crosswords/cot_sampling.sh            |    1 -
 .../crosswords/search_crosswords-dfs.ipynb    |   10 +-
 scripts/crosswords/standard_sampling.sh       |    1 -
 scripts/game24/bfs.sh                         |    1 -
 scripts/game24/cot_sampling.sh                |    1 -
 scripts/game24/standard_sampling.sh           |    1 -
 scripts/text/bfs.sh                           |    3 +-
 scripts/text/cot_sampling.sh                  |    3 +-
 scripts/text/standard_sampling.sh             |    3 +-
 setup.py                                      |   37 +
 src/tot/__init__.py                           |    1 +
 {data => src/tot/data}/24/24.csv              | 2724 ++++++++---------
 .../tot/data}/crosswords/mini0505.json        |    0
 .../data}/crosswords/mini0505_0_100_5.json    |    0
 .../tot/data}/text/data_100_random_text.txt   |    0
 src/tot/methods/bfs.py                        |   96 +
 models.py => src/tot/models.py                |    2 +-
 {prompts => src/tot/prompts}/crosswords.py    |    0
 {prompts => src/tot/prompts}/game24.py        |    0
 {prompts => src/tot/prompts}/text.py          |    0
 src/tot/tasks/__init__.py                     |   12 +
 {tasks => src/tot/tasks}/base.py              |    3 +-
 {tasks => src/tot/tasks}/crosswords.py        |   11 +-
 {tasks => src/tot/tasks}/game24.py            |    4 +-
 {tasks => src/tot/tasks}/text.py              |    6 +-
 tasks/__init__.py                             |   12 -
 33 files changed, 1579 insertions(+), 1502 deletions(-)
 create mode 100644 MANIFEST.in
 rename fake.png => pics/fake.png (100%)
 rename teaser.png => pics/teaser.png (100%)
 create mode 100644 pyproject.toml
 create mode 100644 setup.py
 create mode 100644 src/tot/__init__.py
 rename {data => src/tot/data}/24/24.csv (97%)
 rename {data => src/tot/data}/crosswords/mini0505.json (100%)
 rename {data => src/tot/data}/crosswords/mini0505_0_100_5.json (100%)
 rename {data => src/tot/data}/text/data_100_random_text.txt (100%)
 create mode 100644 src/tot/methods/bfs.py
 rename models.py => src/tot/models.py (95%)
 rename {prompts => src/tot/prompts}/crosswords.py (100%)
 rename {prompts => src/tot/prompts}/game24.py (100%)
 rename {prompts => src/tot/prompts}/text.py (100%)
 create mode 100644 src/tot/tasks/__init__.py
 rename {tasks => src/tot/tasks}/base.py (73%)
 rename {tasks => src/tot/tasks}/crosswords.py (98%)
 rename {tasks => src/tot/tasks}/game24.py (97%)
 rename {tasks => src/tot/tasks}/text.py (97%)
 delete mode 100644 tasks/__init__.py

diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000..2a1bf9e
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,4 @@
+include src/tot/data/24/24.csv
+include src/tot/data/crosswords/mini0505_0_100_5.json
+include src/tot/data/crosswords/mini0505.json
+include src/tot/data/text/data_100_random_text.txt
diff --git a/fake.png b/pics/fake.png
similarity index 100%
rename from fake.png
rename to pics/fake.png
diff --git a/teaser.png b/pics/teaser.png
similarity index 100%
rename from teaser.png
rename to pics/teaser.png
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..a18b5e3
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,35 @@
+[build-system]
+requires = ["setuptools >= 61.0.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "tot"
+version = "0.1.0"
+description = 'Official Implementation of "Tree of Thoughts: Deliberate Problem Solving with Large Language Models"'
+readme = "README.md"
+requires-python = ">= 3.7"
+authors = [{ name = "Shunyu Yao", email = "shunyuyao.cs@gmail.com" }]
+license = { text = "MIT License" }
+keywords = ["tree-search", "large-language-models", "llm", "prompting", "tree-of-thoughts"]
+classifiers = [
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.7",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    'Intended Audience :: Science/Research',
+    'Topic :: Scientific/Engineering :: Artificial Intelligence',
+]
+dynamic=["dependencies"]
+
+
+[tool.setuptools.dynamic]
+dependencies = {file = ["requirements.txt"]}
+
+[tool.setuptools.packages.find]
+where = ["src"]  # list of folders that contain the packages (["."] by default)
+
+[project.urls]
+Homepage = "https://github.com/princeton-nlp/tree-of-thought-llm"
\ No newline at end of file
diff --git a/readme.md b/readme.md
index d4cf54e..c16839c 100644
--- a/readme.md
+++ b/readme.md
@@ -4,7 +4,7 @@
 <details>
   <summary>Note: https://github.com/kyegomez/tree-of-thoughts is NOT the correct implementation to replicate paper results. </summary>
   
-In fact, people have reported that his code [cannot](https://github.com/kyegomez/tree-of-thoughts/issues/52) [properly](https://github.com/kyegomez/tree-of-thoughts/issues/41) [run](https://github.com/kyegomez/tree-of-thoughts/issues/60), and is [probably automatically generated by ChatGPT](fake.png), and [kyegomez has done so for other popular ML methods](https://twitter.com/qbitium/status/1663954096741814272), while intentionally refusing to link to official implementations for his own interests (see https://github.com/kyegomez/tree-of-thoughts/issues/54, https://github.com/kyegomez/tree-of-thoughts/issues/55, https://github.com/kyegomez/tree-of-thoughts/issues/56).
+In fact, people have reported that his code [cannot](https://github.com/kyegomez/tree-of-thoughts/issues/52) [properly](https://github.com/kyegomez/tree-of-thoughts/issues/41) [run](https://github.com/kyegomez/tree-of-thoughts/issues/60), and is [probably automatically generated by ChatGPT](pics/fake.png), and [kyegomez has done so for other popular ML methods](https://twitter.com/qbitium/status/1663954096741814272), while intentionally refusing to link to official implementations for his own interests (see https://github.com/kyegomez/tree-of-thoughts/issues/54, https://github.com/kyegomez/tree-of-thoughts/issues/55, https://github.com/kyegomez/tree-of-thoughts/issues/56).
 Unfortunately, Google/Github searches go to kyegomez's malicious repo by default as it has more stars. **Please DE-STAR his repo and STAR this to help other people avoid being misled, thanks!**
 </details>
 
@@ -12,7 +12,7 @@ Unfortunately, Google/Github searches go to kyegomez's malicious repo by default
 
 
 
-![teaser](teaser.png)
+![teaser](pics/teaser.png)
 
 Official implementation for paper [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://arxiv.org/abs/2305.10601) with code, prompts, model outputs.
 Also check [its tweet thread](https://twitter.com/ShunyuYao12/status/1659357547474681857) in 1min.
diff --git a/requirements.txt b/requirements.txt
index a75d3de..10f2db9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -16,3 +16,4 @@ sympy==1.12
 tqdm==4.65.0
 urllib3==2.0.2
 yarl==1.9.2
+pandas==2.0.3
\ No newline at end of file
diff --git a/run.py b/run.py
index c3ae2a7..e36712a 100644
--- a/run.py
+++ b/run.py
@@ -1,108 +1,18 @@
 import os
 import json
-import itertools
 import argparse
-import numpy as np
-from functools import partial
-from models import gpt, gpt_usage
-from tasks import get_task
 
-def get_value(task, x, y, n_evaluate_sample, cache_value=True):
-    value_prompt = task.value_prompt_wrap(x, y)
-    if cache_value and value_prompt in task.value_cache:
-        return task.value_cache[value_prompt]
-    value_outputs = gpt(value_prompt, n=n_evaluate_sample, stop=None)
-    value = task.value_outputs_unwrap(x, y, value_outputs)
-    if cache_value:
-        task.value_cache[value_prompt] = value
-    return value
-
-def get_values(task, x, ys, n_evaluate_sample, cache_value=True):
-    values = []
-    local_value_cache = {}
-    for y in ys:  # each partial output
-        if y in local_value_cache:  # avoid duplicate candidates
-            value = 0
-        else:    
-            value = get_value(task, x, y, n_evaluate_sample, cache_value=cache_value)
-            local_value_cache[y] = value
-        values.append(value)
-    return values
-
-def get_votes(task, x, ys, n_evaluate_sample):
-    vote_prompt = task.vote_prompt_wrap(x, ys)
-    vote_outputs = gpt(vote_prompt, n=n_evaluate_sample, stop=None)
-    values = task.vote_outputs_unwrap(vote_outputs, len(ys))
-    return values
-
-def get_proposals(task, x, y): 
-    propose_prompt = task.propose_prompt_wrap(x, y)
-    proposals = gpt(propose_prompt, n=1, stop=None)[0].split('\n')
-    return [y + _ + '\n' for _ in proposals]
-
-def get_samples(task, x, y, n_generate_sample, prompt_sample, stop):
-    if prompt_sample == 'standard':
-        prompt = task.standard_prompt_wrap(x, y)
-    elif prompt_sample == 'cot':
-        prompt = task.cot_prompt_wrap(x, y)
-    else:
-        raise ValueError(f'prompt_sample {prompt_sample} not recognized')
-    samples = gpt(prompt, n=n_generate_sample, stop=stop)
-    return [y + _ for _ in samples]
-
-def solve(args, task, idx, to_print=True):
-    print(gpt)
-    x = task.get_input(idx)  # input
-    ys = ['']  # current output candidates
-    infos = []
-    for step in range(task.steps):
-        # generation
-        if args.method_generate == 'sample':
-            new_ys = [get_samples(task, x, y, args.n_generate_sample, prompt_sample=args.prompt_sample, stop=task.stops[step]) for y in ys]
-        elif args.method_generate == 'propose':
-            new_ys = [get_proposals(task, x, y) for y in ys]
-        new_ys = list(itertools.chain(*new_ys))
-        ids = list(range(len(new_ys)))
-        # evaluation
-        if args.method_evaluate == 'vote':
-            values = get_votes(task, x, new_ys, args.n_evaluate_sample)
-        elif args.method_evaluate == 'value':
-            values = get_values(task, x, new_ys, args.n_evaluate_sample)
-
-        # selection
-        if args.method_select == 'sample':
-            ps = np.array(values) / sum(values)
-            select_ids = np.random.choice(ids, size=args.n_select_sample, p=ps).tolist()
-        elif args.method_select == 'greedy':
-            select_ids = sorted(ids, key=lambda x: values[x], reverse=True)[:args.n_select_sample]
-        select_new_ys = [new_ys[select_id] for select_id in select_ids]
-
-        # log
-        if to_print: 
-            sorted_new_ys, sorted_values = zip(*sorted(zip(new_ys, values), key=lambda x: x[1], reverse=True))
-            print(f'-- new_ys --: {sorted_new_ys}\n-- sol values --: {sorted_values}\n-- choices --: {select_new_ys}\n')
-        
-        infos.append({'step': step, 'x': x, 'ys': ys, 'new_ys': new_ys, 'values': values, 'select_new_ys': select_new_ys})
-        ys = select_new_ys
-    
-    if to_print: 
-        print(ys)
-    return ys, {'steps': infos}
-
-def naive_solve(args, task, idx, to_print=True):
-    x = task.get_input(idx)  # input
-    ys = get_samples(task, x, '', args.n_generate_sample, args.prompt_sample, stop=None)
-    return ys, {}
+from tot.tasks import get_task
+from tot.methods.bfs import solve, naive_solve
+from tot.models import gpt_usage
 
 def run(args):
-    task = get_task(args.task, args.task_file_path)
+    task = get_task(args.task)
     logs, cnt_avg, cnt_any = [], 0, 0
-    global gpt
-    gpt = partial(gpt, model=args.backend, temperature=args.temperature)
     if args.naive_run:
-        file = f'logs/{args.task}/{args.backend}_{args.temperature}_naive_{args.prompt_sample}_sample_{args.n_generate_sample}_start{args.task_start_index}_end{args.task_end_index}.json'
+        file = f'./logs/{args.task}/{args.backend}_{args.temperature}_naive_{args.prompt_sample}_sample_{args.n_generate_sample}_start{args.task_start_index}_end{args.task_end_index}.json'
     else:
-        file = f'logs/{args.task}/{args.backend}_{args.temperature}_{args.method_generate}{args.n_generate_sample}_{args.method_evaluate}{args.n_evaluate_sample}_{args.method_select}{args.n_select_sample}_start{args.task_start_index}_end{args.task_end_index}.json'
+        file = f'./logs/{args.task}/{args.backend}_{args.temperature}_{args.method_generate}{args.n_generate_sample}_{args.method_evaluate}{args.n_evaluate_sample}_{args.method_select}{args.n_select_sample}_start{args.task_start_index}_end{args.task_end_index}.json'
     os.makedirs(os.path.dirname(file), exist_ok=True)
 
     for i in range(args.task_start_index, args.task_end_index):
@@ -136,7 +46,6 @@ def parse_args():
     args.add_argument('--temperature', type=float, default=0.7)
 
     args.add_argument('--task', type=str, required=True, choices=['game24', 'text', 'crosswords'])
-    args.add_argument('--task_file_path', type=str, required=True)
     args.add_argument('--task_start_index', type=int, default=900)
     args.add_argument('--task_end_index', type=int, default=1000)
 
@@ -145,7 +54,7 @@ def parse_args():
 
     args.add_argument('--method_generate', type=str, choices=['sample', 'propose'])
     args.add_argument('--method_evaluate', type=str, choices=['value', 'vote'])
-    args.add_argument('--method_select', type=str, choices=['sample', 'greedy'])
+    args.add_argument('--method_select', type=str, choices=['sample', 'greedy'], default='greedy')
     args.add_argument('--n_generate_sample', type=int, default=1)  # only thing needed if naive_run
     args.add_argument('--n_evaluate_sample', type=int, default=1)
     args.add_argument('--n_select_sample', type=int, default=1)
diff --git a/scripts/crosswords/cot_sampling.sh b/scripts/crosswords/cot_sampling.sh
index 3e03130..3b8e8d3 100644
--- a/scripts/crosswords/cot_sampling.sh
+++ b/scripts/crosswords/cot_sampling.sh
@@ -1,6 +1,5 @@
 python run.py \
     --task crosswords \
-    --task_file_path mini0505_0_100_5.json \
     --task_start_index 0 \
     --task_end_index 20 \
     --naive_run \
diff --git a/scripts/crosswords/search_crosswords-dfs.ipynb b/scripts/crosswords/search_crosswords-dfs.ipynb
index ba93607..4c5f6ca 100644
--- a/scripts/crosswords/search_crosswords-dfs.ipynb
+++ b/scripts/crosswords/search_crosswords-dfs.ipynb
@@ -14,7 +14,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cd ../.."
+    "cd .."
    ]
   },
   {
@@ -24,9 +24,9 @@
    "outputs": [],
    "source": [
     "import json\n",
-    "from prompts.crosswords import propose_prompt, value_prompt\n",
-    "from models import gpt\n",
-    "from tasks.crosswords import MiniCrosswordsEnv\n",
+    "from tot.prompts.crosswords import propose_prompt, value_prompt\n",
+    "from tot.models import gpt\n",
+    "from tot.tasks.crosswords import MiniCrosswordsEnv\n",
     "\n",
     "env = MiniCrosswordsEnv()"
    ]
@@ -61,7 +61,7 @@
    "source": [
     "import re\n",
     "import copy\n",
-    "from models import gpt\n",
+    "from tot.models import gpt\n",
     "\n",
     "def parse_line(input_str):\n",
     "    # regular expression pattern to match the input string format\n",
diff --git a/scripts/crosswords/standard_sampling.sh b/scripts/crosswords/standard_sampling.sh
index 094b639..79effd6 100644
--- a/scripts/crosswords/standard_sampling.sh
+++ b/scripts/crosswords/standard_sampling.sh
@@ -1,6 +1,5 @@
 python run.py \
     --task crosswords \
-    --task_file_path mini0505_0_100_5.json \
     --task_start_index 0 \
     --task_end_index 20 \
     --naive_run \
diff --git a/scripts/game24/bfs.sh b/scripts/game24/bfs.sh
index baa5a9e..e540fc1 100644
--- a/scripts/game24/bfs.sh
+++ b/scripts/game24/bfs.sh
@@ -1,6 +1,5 @@
 python run.py \
     --task game24 \
-    --task_file_path 24.csv \
     --task_start_index 900 \
     --task_end_index 1000 \
     --method_generate propose \
diff --git a/scripts/game24/cot_sampling.sh b/scripts/game24/cot_sampling.sh
index 464c957..cbd883c 100644
--- a/scripts/game24/cot_sampling.sh
+++ b/scripts/game24/cot_sampling.sh
@@ -1,6 +1,5 @@
 python run.py \
     --task game24 \
-    --task_file_path 24.csv \
     --task_start_index 900 \
     --task_end_index 1000 \
     --naive_run \
diff --git a/scripts/game24/standard_sampling.sh b/scripts/game24/standard_sampling.sh
index 058b969..4f80361 100644
--- a/scripts/game24/standard_sampling.sh
+++ b/scripts/game24/standard_sampling.sh
@@ -1,6 +1,5 @@
 python run.py \
     --task game24 \
-    --task_file_path 24.csv \
     --task_start_index 900 \
     --task_end_index 1000 \
     --naive_run \
diff --git a/scripts/text/bfs.sh b/scripts/text/bfs.sh
index dff7452..133023b 100644
--- a/scripts/text/bfs.sh
+++ b/scripts/text/bfs.sh
@@ -1,8 +1,7 @@
 python run.py \
     --task text \
-    --task_file_path data_100_random_text.txt \
     --task_start_index 0 \
-    --task_end_index 1 \
+    --task_end_index 100 \
     --method_generate sample \
     --method_evaluate vote \
     --method_select greedy \
diff --git a/scripts/text/cot_sampling.sh b/scripts/text/cot_sampling.sh
index 8f2c6f6..8b95ef6 100644
--- a/scripts/text/cot_sampling.sh
+++ b/scripts/text/cot_sampling.sh
@@ -1,8 +1,7 @@
 python run.py \
     --task text \
-    --task_file_path data_100_random_text.txt \
     --task_start_index 0 \
-    --task_end_index 1 \
+    --task_end_index 100 \
     --naive_run \
     --prompt_sample cot \
     --n_generate_sample 10 \
diff --git a/scripts/text/standard_sampling.sh b/scripts/text/standard_sampling.sh
index a24fd5c..cc65b84 100644
--- a/scripts/text/standard_sampling.sh
+++ b/scripts/text/standard_sampling.sh
@@ -1,8 +1,7 @@
 python run.py \
     --task text \
-    --task_file_path data_100_random_text.txt \
     --task_start_index 0 \
-    --task_end_index 1 \
+    --task_end_index 100 \
     --naive_run \
     --prompt_sample standard \
     --n_generate_sample 10 \
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..5dc64ab
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,37 @@
+import setuptools
+
+with open('README.md', 'r', encoding='utf-8') as fh:
+    long_description = fh.read()
+
+
+setuptools.setup(
+    name='tot',
+    author='Shunyu Yao',
+    author_email='shunyuyao.cs@gmail.com',
+    description='Official Implementation of "Tree of Thoughts: Deliberate Problem Solving with Large Language Models"',
+    keywords='tree-search, large-language-models, llm, prompting, tree-of-thoughts',
+    long_description=long_description,
+    long_description_content_type='text/markdown',
+    url='https://github.com/princeton-nlp/tree-of-thought-llm',
+    project_urls={
+        'Homepage': 'https://github.com/princeton-nlp/tree-of-thought-llm',
+    },
+    package_dir={'': 'src'},
+    packages=setuptools.find_packages(where='src'),
+    classifiers=[
+        "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
+        'Intended Audience :: Science/Research',
+        'Topic :: Scientific/Engineering :: Artificial Intelligence',
+    ],
+    python_requires='>=3.7',
+    install_requires=[
+        'setuptools',
+    ],
+    include_package_data=True,
+)
\ No newline at end of file
diff --git a/src/tot/__init__.py b/src/tot/__init__.py
new file mode 100644
index 0000000..a68927d
--- /dev/null
+++ b/src/tot/__init__.py
@@ -0,0 +1 @@
+__version__ = "0.1.0"
\ No newline at end of file
diff --git a/data/24/24.csv b/src/tot/data/24/24.csv
similarity index 97%
rename from data/24/24.csv
rename to src/tot/data/24/24.csv
index 19aa0a6..74f1292 100644
--- a/data/24/24.csv
+++ b/src/tot/data/24/24.csv
@@ -1,1363 +1,1363 @@
-Rank,Puzzles,AMT (s),Solved rate,1-sigma Mean (s),1-sigma STD (s)
-1,1 1 4 6,4.4,99.20%,4.67,1.48
-2,1 1 11 11,4.41,99.60%,4.68,1.45
-3,1 1 3 8,4.45,99.20%,4.69,1.48
-4,1 1 1 8,4.48,98.80%,4.66,1.25
-5,6 6 6 6,4.59,99.40%,4.82,1.49
-6,1 1 2 12,4.63,99.10%,4.95,1.57
-7,1 2 2 6,4.8,99.20%,5.16,1.61
-8,1 1 10 12,4.81,99%,5.06,1.54
-9,2 2 10 10,4.85,98.20%,5.13,1.63
-10,1 1 1 12,4.86,99.20%,5.18,1.63
-11,1 1 2 8,4.96,97.80%,5.31,1.76
-12,1 1 4 8,4.99,98.10%,5.35,1.83
-13,1 1 5 8,5,96.40%,5.36,1.81
-14,4 6 11 11,5,98.90%,5.38,1.73
-15,1 1 3 12,5.02,97.30%,5.36,1.84
-16,2 2 2 12,5.02,99.10%,5.37,1.7
-17,1 1 4 12,5.03,97.40%,5.45,1.85
-18,1 1 12 12,5.03,99.40%,5.4,1.66
-19,3 3 3 8,5.04,98%,5.4,1.8
-20,1 1 2 6,5.09,98.40%,5.4,1.61
-21,1 1 2 11,5.09,98.90%,5.41,1.7
-22,1 2 3 4,5.1,99%,5.46,1.85
-23,11 11 12 12,5.1,98.70%,5.5,1.87
-24,3 7 7 8,5.11,96.70%,5.48,1.89
-25,1 1 13 13,5.16,99.30%,5.45,1.66
-26,1 2 4 12,5.16,97.60%,5.56,1.99
-27,1 1 3 6,5.17,97.70%,5.52,1.77
-28,1 1 3 9,5.17,97%,5.52,1.85
-29,7 7 12 12,5.17,98.60%,5.58,1.89
-30,4 6 7 7,5.18,98.60%,5.53,1.83
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-33,1 1 11 13,5.23,99.10%,5.66,1.85
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-35,4 5 12 12,5.24,97%,5.6,1.79
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-37,12 12 12 12,5.25,99.30%,5.73,2.14
-38,2 11 11 12,5.26,97.20%,5.68,2
-39,4 4 4 6,5.26,98.10%,5.61,1.94
-40,1 1 1 11,5.27,97.30%,5.56,1.66
-41,1 1 11 12,5.27,99.30%,5.67,1.8
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-43,1 5 7 12,5.3,98.30%,5.87,2.12
-44,10 10 12 12,5.31,98.60%,5.74,1.91
-45,1 8 8 8,5.32,97.70%,5.65,1.94
-46,2 2 3 8,5.33,98.10%,5.72,1.93
-47,2 9 9 12,5.33,97.30%,5.74,2
-48,11 11 11 12,5.33,94.80%,5.61,1.99
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-54,1 6 8 12,5.38,96.80%,5.77,2
-55,8 8 12 12,5.38,98.50%,5.8,1.95
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-57,5 6 12 12,5.39,97.10%,5.78,1.81
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-60,1 1 12 13,5.42,98.70%,5.8,1.84
-61,1 3 5 12,5.42,96.40%,5.79,2.1
-62,5 5 12 12,5.42,98.70%,5.78,1.75
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-68,2 5 5 12,5.46,98.70%,5.87,1.99
-69,11 11 11 13,5.48,98.40%,6.03,2.26
-70,2 12 13 13,5.49,97.10%,5.85,2.04
-71,7 7 11 12,5.49,93.70%,5.78,2.05
-72,1 1 3 7,5.5,96.80%,5.82,1.95
-73,1 4 10 10,5.5,97.90%,5.91,2.1
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-75,1 3 4 12,5.52,98.50%,5.95,1.97
-76,5 5 11 12,5.52,96.50%,5.89,1.9
-77,1 2 5 8,5.54,95.50%,5.94,2.26
-78,2 2 4 6,5.54,99%,5.92,1.9
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-80,1 8 9 12,5.56,96.50%,5.87,1.96
-81,6 7 12 12,5.56,95.60%,5.93,2.03
-82,1 3 10 10,5.57,97.40%,5.87,2
-83,2 3 3 8,5.57,96.90%,5.94,1.94
-84,3 5 5 8,5.57,96.90%,5.97,2.08
-85,1 1 1 13,5.58,97.40%,5.9,1.77
-86,2 3 12 12,5.58,97.40%,5.97,2
-87,1 4 7 12,5.59,98.30%,5.99,2.02
-88,8 8 11 13,5.6,97%,6.1,2.29
-89,1 3 3 4,5.61,97.40%,5.86,1.79
-90,1 8 8 12,5.61,96.30%,5.91,1.9
-91,3 7 8 8,5.63,95.90%,5.99,2.03
-92,7 8 12 12,5.63,96.10%,6.07,2.12
-93,9 9 11 12,5.63,93.90%,5.98,2.15
-94,1 2 5 12,5.64,95.40%,6.09,2.2
-95,2 7 7 8,5.64,96.80%,5.94,1.99
-96,4 4 11 13,5.64,97.90%,6.25,2.36
-97,1 1 4 7,5.65,97.10%,5.95,2.07
-98,1 1 10 13,5.65,97.90%,6.09,2.2
-99,4 6 6 6,5.65,98.30%,6.02,2.07
-100,5 5 7 7,5.65,97.80%,5.97,1.91
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-105,1 1 2 10,5.69,95.60%,5.96,1.81
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-109,4 8 11 11,5.7,95.10%,6.01,1.98
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-111,1 6 6 11,5.71,98.20%,6.18,2.24
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-352,3 4 5 7,6.81,96.60%,7.28,2.65
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-354,4 8 8 8,6.81,94.60%,7.16,2.75
-355,5 6 13 13,6.81,86.90%,6.91,2.69
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-358,2 4 4 8,6.82,96.40%,7.23,2.67
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-362,7 9 13 13,6.83,96.30%,7.42,2.78
-363,9 11 11 11,6.83,95.30%,7.21,2.76
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-381,6 8 11 11,6.9,94.20%,7.43,3.06
-382,9 10 11 12,6.9,96.10%,7.41,2.76
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-388,1 2 11 12,6.93,96.90%,7.63,3.2
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-393,4 7 8 8,6.94,94%,7.33,2.53
-394,5 5 10 10,6.94,86.70%,7.24,3.12
-395,1 2 7 10,6.95,90.90%,7.71,4
-396,2 6 10 12,6.95,95.10%,7.43,2.81
-397,2 6 7 13,6.96,96.60%,7.37,2.66
-398,2 9 9 11,6.96,87.30%,6.96,2.68
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-411,5 5 10 13,6.99,89.70%,7.34,3.26
-412,6 8 8 8,6.99,88.10%,7.38,3.47
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-419,1 3 5 13,7.02,94.10%,7.43,2.72
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-421,2 9 9 13,7.02,88.60%,7.08,2.5
-422,1 7 7 9,7.03,96.30%,7.4,2.83
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-424,8 8 9 11,7.03,89.70%,7.57,3.69
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-680,1 6 11 12,8.28,88.80%,8.26,2.97
-681,2 5 10 11,8.28,94.10%,8.85,3.5
-682,5 6 9 10,8.28,90.80%,8.74,3.78
-683,1 3 4 9,8.3,94.50%,8.8,3.81
-684,3 4 7 10,8.3,93.30%,8.54,3.42
-685,6 6 7 9,8.3,90.70%,8.63,3.71
-686,6 8 8 11,8.3,87.70%,8.55,3.82
-687,2 4 4 4,8.31,94.60%,8.76,3.19
-688,2 9 10 11,8.33,88.70%,8.3,3.31
-689,4 4 8 11,8.33,87%,8.32,3.69
-690,4 4 4 9,8.34,88.30%,8.43,3.94
-691,6 8 10 13,8.36,86.60%,8.43,3.64
-692,2 8 8 11,8.37,84%,8.09,3.45
-693,4 7 8 9,8.37,92.10%,8.77,3.69
-694,1 6 12 13,8.38,89.40%,8.45,3.24
-695,4 4 7 12,8.38,89.40%,8.75,4.13
-696,6 6 8 9,8.38,90.30%,8.64,3.44
-697,1 2 2 7,8.39,91.70%,8.72,3.35
-698,1 2 6 13,8.4,90.60%,8.88,3.9
-699,2 3 3 7,8.4,92.10%,8.85,3.69
-700,2 4 7 10,8.4,91.20%,8.67,3.82
-701,2 9 12 13,8.4,90.60%,8.82,3.86
-702,6 6 9 11,8.4,88.80%,8.82,3.98
-703,3 3 7 11,8.41,92.20%,8.7,3.56
-704,3 3 8 12,8.41,91.70%,8.97,3.88
-705,1 4 9 10,8.42,93.20%,8.85,3.63
-706,3 6 8 9,8.42,93.80%,8.82,3.16
-707,4 4 6 11,8.42,86.30%,8.51,4.42
-708,1 6 10 13,8.44,87.60%,8.63,4.1
-709,1 3 4 10,8.45,92.90%,8.73,3.35
-710,2 9 10 13,8.45,90.20%,9.07,4.07
-711,5 6 10 13,8.45,91.50%,9.12,4.24
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-714,7 8 8 11,8.48,80.60%,8.17,4.53
-715,1 2 7 8,8.49,94.30%,9.01,3.53
-716,1 11 13 13,8.49,91.30%,8.83,3.74
-717,1 4 6 7,8.5,87.30%,8.78,4.49
-718,2 10 11 13,8.5,89.50%,8.58,3.43
-719,5 7 8 8,8.5,82.70%,8.43,4.67
-720,2 2 3 13,8.51,87.60%,8.54,3.6
-721,2 3 10 13,8.51,93.40%,9.02,3.94
-722,2 4 6 6,8.52,94.10%,9.27,3.98
-723,4 5 6 7,8.54,90.30%,8.94,3.82
-724,4 5 8 12,8.54,93.30%,9.08,3.71
-725,3 6 7 12,8.55,90%,8.81,3.76
-726,3 7 7 13,8.56,93.40%,8.84,3.13
-727,1 2 9 13,8.57,93.30%,9.21,4.17
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-729,4 4 9 11,8.57,82.50%,8.26,4.41
-730,1 2 3 11,8.58,95.70%,8.98,3.39
-731,2 4 6 9,8.58,91.80%,9.06,4.19
-732,2 8 11 12,8.58,88.30%,8.82,4.18
-733,4 4 4 11,8.58,86%,8.81,4.65
-734,1 2 3 3,8.59,95.50%,8.93,2.98
-735,4 5 9 13,8.59,89.30%,9.33,4.75
-736,4 6 10 12,8.59,93.80%,9.17,3.92
-737,2 4 9 9,8.6,90%,8.71,3.82
-738,2 6 6 10,8.61,94.10%,9.05,3.64
-739,1 2 5 13,8.62,94.30%,9.16,3.68
-740,6 8 9 13,8.62,93.90%,9.3,3.96
-741,2 9 11 11,8.63,89.30%,9.08,4.03
-742,1 3 9 13,8.65,93.70%,9.25,4.13
-743,2 2 8 10,8.65,94.30%,9.26,3.29
-744,2 4 7 11,8.66,92.50%,8.98,3.79
-745,2 6 7 8,8.66,89.60%,8.97,3.88
-746,1 3 4 7,8.67,95%,9.48,3.83
-747,4 8 9 11,8.68,93%,9.16,3.89
-748,6 6 8 10,8.68,90.10%,8.9,3.96
-749,5 5 9 10,8.69,86.90%,9.06,4.3
-750,2 3 7 11,8.7,91.70%,9.09,4.21
-751,2 8 9 11,8.7,90.50%,9.1,3.77
-752,2 4 10 13,8.71,88.90%,9.07,4.43
-753,1 3 8 8,8.72,86.50%,8.95,4.51
-754,3 9 9 10,8.73,86.70%,8.78,3.81
-755,4 11 12 13,8.74,91.20%,9.18,3.87
-756,1 2 6 11,8.75,90%,9.19,4.25
-757,2 5 7 10,8.75,92.20%,9.15,3.72
-758,3 4 7 7,8.75,94%,9.41,3.8
-759,6 8 12 12,8.77,91.50%,9.48,4.23
-760,2 3 5 9,8.79,92%,9.31,4.31
-761,4 4 8 10,8.8,91.40%,9.13,4.36
-762,3 7 10 13,8.82,88.20%,9.22,4.63
-763,4 5 8 8,8.82,86.40%,8.8,4.02
-764,3 4 4 5,8.83,91.10%,9.13,3.95
-765,3 5 9 12,8.83,90%,9.14,4.01
-766,4 8 8 12,8.83,94.40%,9.39,3.84
-767,4 12 12 12,8.83,86%,9.14,4.72
-768,2 2 5 6,8.84,88.60%,9.16,4.38
-769,9 10 11 13,8.84,89.70%,9.09,3.8
-770,2 3 4 13,8.85,90.90%,9.19,3.9
-771,3 10 11 12,8.85,89.40%,9.04,3.71
-772,4 4 8 13,8.85,86.70%,8.78,4.2
-773,5 6 6 10,8.85,88.90%,9.06,3.86
-774,1 1 3 10,8.86,88.10%,8.88,3.86
-775,1 2 4 5,8.86,90.40%,9.27,4.21
-776,1 4 5 10,8.86,91.20%,9.01,3.61
-777,2 5 8 13,8.87,93%,9.39,3.91
-778,3 5 6 9,8.88,92.50%,9.31,3.67
-779,7 8 10 13,8.89,92.10%,9.4,4.08
-780,6 6 8 13,8.9,83.80%,8.68,4.24
-781,3 9 10 12,8.92,90.20%,9.17,3.63
-782,1 2 4 9,8.93,90.80%,9.28,3.92
-783,1 3 3 10,8.94,89.60%,9.04,4.1
-784,1 5 5 9,8.94,88.40%,9.09,3.7
-785,1 4 6 9,8.95,86.10%,8.81,4.02
-786,2 3 9 9,8.96,90.50%,9.32,3.95
-787,4 4 5 10,8.96,86.30%,9.07,4.66
-788,6 6 6 12,8.96,89.50%,9.18,4.48
-789,1 3 5 10,8.97,93.20%,9.46,3.7
-790,2 11 12 13,8.97,89.80%,9.34,3.94
-791,2 2 5 7,8.98,88.90%,9.03,3.68
-792,8 8 9 13,8.98,81.20%,8.59,4.69
-793,2 2 3 6,8.99,91.60%,9.28,4.01
-794,3 4 7 11,8.99,87.30%,9.05,4.58
-795,3 5 6 7,8.99,89.50%,9.04,3.8
-796,4 5 5 9,8.99,91.30%,9.32,3.88
-797,4 7 10 10,8.99,83.90%,8.74,4.08
-798,3 6 12 13,9,86.30%,8.93,4.31
-799,5 5 6 7,9,90.80%,9.44,4.03
-800,6 8 11 13,9,86.30%,9.38,4.5
-801,1 5 9 11,9.02,93.10%,9.53,3.93
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-803,7 12 12 13,9.05,82.60%,8.84,4.09
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-805,1 5 6 8,9.1,90%,9.15,3.84
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-807,2 2 5 11,9.1,87.40%,9.42,4.41
-808,2 5 8 9,9.1,92.50%,9.42,3.73
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-810,2 3 6 10,9.11,92.20%,9.33,3.75
-811,1 3 5 7,9.13,90%,9.38,4.02
-812,1 5 10 13,9.13,88.60%,9.15,3.43
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-815,8 8 8 13,9.19,84.70%,9.17,4.29
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-819,4 7 7 11,9.22,87.50%,9.49,4.52
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-822,5 6 7 12,9.23,84.60%,9.15,4.29
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-824,3 3 3 9,9.25,84.50%,9.27,4.67
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-826,1 9 11 13,9.26,88%,9.47,4.01
-827,2 2 6 8,9.27,95.50%,9.69,3.59
-828,5 8 9 12,9.29,92.30%,9.72,3.87
-829,1 1 4 10,9.3,85.60%,9.15,5.02
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-831,2 3 3 9,9.31,93.20%,9.71,3.76
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-834,3 4 6 12,9.34,93.30%,9.85,3.99
-835,3 7 7 10,9.34,89%,9.62,4.27
-836,3 9 11 12,9.35,90.10%,9.79,4.37
-837,2 4 4 7,9.37,89.40%,9.55,4.27
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-839,4 5 7 12,9.4,87.50%,9.33,3.91
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-841,5 9 9 11,9.44,89.50%,9.64,3.84
-842,6 6 6 8,9.44,91.40%,9.75,4.17
-843,1 7 12 12,9.45,80.60%,8.78,4.34
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-845,2 5 6 10,9.46,88%,9.51,4.42
-846,3 8 9 12,9.46,90.20%,9.87,4.4
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-848,2 4 5 9,9.49,86.30%,9.52,4.62
-849,4 7 11 12,9.49,86.30%,9.64,4.7
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-851,2 6 8 8,9.51,92.50%,9.87,4.08
-852,4 5 5 7,9.51,82.40%,9.15,4.34
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-854,4 7 8 13,9.53,90.70%,9.97,4.28
-855,6 6 8 12,9.53,90.80%,9.88,4.21
-856,5 6 8 9,9.54,89.90%,9.82,4.35
-857,3 3 5 9,9.55,87%,9.56,3.99
-858,4 5 7 11,9.55,87.30%,9.77,4.97
-859,4 9 12 12,9.56,89.40%,10.06,4.28
-860,3 3 6 9,9.57,94.20%,10.28,4.1
-861,3 5 9 13,9.57,91.30%,9.91,4.01
-862,3 7 8 13,9.57,84%,9.5,4.79
-863,4 4 5 7,9.57,85.10%,9.27,4.41
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-865,2 4 6 13,9.65,87.20%,9.94,4.99
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-869,2 3 4 10,9.72,91.90%,10.1,4.16
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-871,4 9 9 10,9.73,89.90%,9.98,4.04
-872,3 3 7 9,9.74,90.60%,9.91,3.75
-873,1 7 9 10,9.78,86.80%,9.74,3.86
-874,2 3 9 13,9.79,87.50%,10.07,5.28
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-885,4 6 7 10,9.88,86.50%,10.04,4.72
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-888,5 7 9 13,9.94,90.90%,10.39,4.07
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-891,7 8 9 13,9.96,84%,9.71,4.26
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-905,6 7 8 9,10.03,84.50%,9.67,4.35
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-929,4 7 9 13,10.29,84.30%,10.35,5.72
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-934,6 9 9 11,10.33,82.40%,9.64,5
-935,5 8 11 12,10.34,86.90%,10.5,4.16
-936,5 6 8 10,10.35,87%,10.31,4.38
-937,6 11 12 13,10.36,80.10%,9.55,4.95
-938,2 2 8 8,10.38,91.40%,10.72,4.71
-939,2 7 12 13,10.44,80.40%,9.67,4.84
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-941,3 4 9 13,10.45,79.60%,9.45,5.34
-942,4 5 10 12,10.45,86.20%,10.27,4.29
-943,1 2 7 11,10.5,88.10%,10.57,4.68
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-945,6 10 12 13,10.5,82.50%,10.05,4.71
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-947,1 4 4 11,10.51,86.70%,10.49,5.21
-948,2 3 9 10,10.55,90%,10.75,4.18
-949,1 2 3 13,10.59,91.70%,10.95,4.79
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-951,1 2 2 9,10.62,82.40%,9.9,4.48
-952,1 3 6 11,10.64,88.10%,10.57,4.29
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-956,7 8 8 12,10.66,83.60%,10.32,4.47
-957,3 4 6 8,10.69,90.70%,11.2,5.02
-958,1 7 9 11,10.71,85.10%,10.44,4.24
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-962,8 8 10 12,10.72,78.70%,9.71,5.6
-963,1 3 8 13,10.73,90.40%,11.06,5.02
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-966,1 9 10 13,10.76,83%,10.25,4.39
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-968,2 5 7 7,10.77,82.90%,10.41,4.71
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-971,4 4 8 12,10.87,92%,11.49,4.93
-972,1 2 6 10,10.89,86.80%,10.8,4.94
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-981,3 4 6 6,11.01,90.50%,11.26,4.63
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-985,2 6 7 11,11.07,82.60%,10.49,5.02
-986,5 9 12 12,11.07,83%,10.77,4.73
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-995,5 8 10 11,11.22,87.10%,10.92,4.73
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-1002,2 4 4 6,11.32,90.20%,11.66,4.67
-1003,2 4 6 8,11.33,93.30%,11.77,4.44
-1004,3 4 6 9,11.33,82.50%,10.75,5.42
-1005,2 3 4 5,11.35,84.40%,10.97,5.57
-1006,3 3 6 12,11.36,86.90%,11.51,6.01
-1007,3 6 7 13,11.36,82.50%,10.76,6.05
-1008,1 4 7 7,11.38,85.50%,11.25,4.6
-1009,4 4 5 11,11.39,85%,11.3,4.82
-1010,6 7 9 9,11.41,80.40%,10.68,5.9
-1011,7 8 10 10,11.42,79.10%,10.55,4.89
-1012,4 8 9 13,11.43,82.70%,10.72,5.97
-1013,1 3 4 8,11.45,89.60%,11.57,4.61
-1014,8 9 9 12,11.45,82.40%,10.81,4.71
-1015,3 7 8 11,11.51,83%,11.11,5.62
-1016,4 6 7 12,11.52,81.40%,11.17,5.36
-1017,1 6 7 9,11.54,84.40%,11.3,4.67
-1018,3 7 9 12,11.54,85.90%,11.56,5.56
-1019,4 5 6 13,11.55,78%,10.18,5.07
-1020,3 3 5 13,11.57,84.90%,11.35,4.69
-1021,2 3 5 7,11.58,88.90%,11.67,4.97
-1022,4 6 8 13,11.61,81%,10.84,5.09
-1023,2 7 9 11,11.62,83.50%,11.26,5.31
-1024,1 3 7 12,11.64,85.40%,10.98,4.98
-1025,3 5 7 9,11.66,86.20%,11.44,4.86
-1026,2 6 10 13,11.67,84.60%,11.3,5.46
-1027,6 8 9 12,11.67,86.60%,11.68,5.06
-1028,1 5 7 9,11.68,84.40%,11.15,4.84
-1029,1 3 7 7,11.69,85.20%,11.44,4.62
-1030,2 9 11 13,11.7,82.20%,11.25,5.36
-1031,3 7 9 11,11.7,88.10%,11.82,4.84
-1032,4 8 8 10,11.71,85.70%,11.63,5.51
-1033,1 4 8 9,11.73,89%,11.9,5.07
-1034,2 5 11 12,11.73,77.10%,10.24,5.29
-1035,2 3 7 13,11.77,86.30%,11.62,5.24
-1036,3 5 10 13,11.79,82.30%,11.04,5.37
-1037,3 5 6 10,11.83,85.80%,11.63,5.59
-1038,4 5 5 10,11.86,81.20%,10.96,4.46
-1039,7 8 9 10,11.86,79.80%,11.04,5.33
-1040,2 5 8 10,11.88,82.90%,11.02,4.82
-1041,4 6 6 12,11.88,86%,11.56,5.8
-1042,5 8 10 12,11.88,84.30%,11.18,5.05
-1043,3 3 3 11,11.91,78%,10.71,5.38
-1044,5 5 8 12,11.91,79.50%,10.98,5.06
-1045,1 3 3 6,11.93,87.20%,11.83,5.16
-1046,2 5 7 13,11.99,83.80%,11.74,5.61
-1047,1 5 8 11,12.07,89.20%,12.42,5.56
-1048,2 3 5 13,12.07,85.50%,11.88,5.34
-1049,3 4 8 9,12.07,88.10%,12.24,5.03
-1050,2 5 5 7,12.09,82%,11.59,4.86
-1051,4 10 11 13,12.16,76.20%,10.51,5.57
-1052,2 6 6 7,12.21,78.40%,10.67,5.54
-1053,7 10 10 12,12.21,78%,11.01,5.07
-1054,2 5 7 11,12.22,82.40%,11.59,5.71
-1055,4 4 6 9,12.24,77.40%,10.48,5.57
-1056,6 9 10 12,12.25,80.50%,11.36,5.94
-1057,6 9 9 12,12.26,87%,12.17,5.82
-1058,2 3 6 12,12.28,91%,12.71,5.2
-1059,2 4 4 5,12.28,82.10%,11.3,5.16
-1060,2 4 4 10,12.28,89.60%,12.37,5.14
-1061,3 3 3 4,12.34,82.40%,11.57,6.01
-1062,2 2 2 7,12.35,73.30%,9.51,5.67
-1063,4 9 10 11,12.38,76.80%,10.69,5.81
-1064,7 9 10 11,12.41,76.90%,10.81,5.32
-1065,1 4 5 7,12.43,87.90%,12.45,5.27
-1066,3 4 9 9,12.43,84.20%,12.03,4.99
-1067,3 3 6 8,12.46,74.60%,10.38,5.58
-1068,4 4 7 8,12.52,83.80%,12.28,5.55
-1069,3 5 11 12,12.56,79.90%,11.32,5.7
-1070,4 4 6 10,12.57,87.50%,12.6,5.35
-1071,4 6 9 13,12.58,85.50%,12.59,5.18
-1072,3 5 6 11,12.59,82.50%,11.93,6.55
-1073,1 5 8 13,12.64,80.10%,11.42,5.73
-1074,3 3 9 13,12.64,74.60%,10.56,5.85
-1075,7 7 9 10,12.64,75.80%,11.23,5.19
-1076,2 4 8 13,12.7,82.20%,12.07,5.92
-1077,4 4 6 8,12.7,84.20%,12.14,6.52
-1078,2 4 5 7,12.71,78.40%,11.3,5.62
-1079,1 4 7 9,12.74,82.20%,12,5.11
-1080,4 7 10 12,12.74,79.80%,11.37,5.54
-1081,4 5 8 13,12.76,78%,11.33,5.89
-1082,5 6 10 12,12.78,75.80%,10.91,5.45
-1083,6 9 11 13,12.78,72.50%,10.29,5.96
-1084,5 7 7 9,12.8,74.20%,11.06,5.06
-1085,1 4 5 9,12.83,81.90%,11.81,6.41
-1086,5 7 11 13,12.83,70.80%,10,5.17
-1087,3 3 5 10,12.84,82.90%,12.05,5.32
-1088,4 8 12 12,12.85,85.60%,12.23,5.83
-1089,2 3 3 3,12.87,82.10%,11.84,5.96
-1090,2 5 10 13,12.92,82.10%,12.4,5.91
-1091,1 4 6 6,12.97,80%,11.77,6.38
-1092,2 3 8 8,12.98,80.40%,11.84,5.7
-1093,6 9 11 12,13.02,75.90%,10.98,5.93
-1094,1 1 6 9,13.05,81%,11.34,6.4
-1095,5 9 10 13,13.05,78%,11.54,5.87
-1096,1 5 6 11,13.06,81%,11.93,6.47
-1097,4 4 5 12,13.09,79%,11.5,5.91
-1098,3 5 6 13,13.1,76%,11.28,5.52
-1099,1 4 5 8,13.16,82.70%,12.46,5.64
-1100,5 5 7 10,13.19,73.80%,11.38,4.96
-1101,2 2 2 9,13.21,76.60%,10.98,6.35
-1102,5 7 10 10,13.28,76.50%,12.05,5.27
-1103,2 3 5 10,13.29,80.20%,12.07,5.35
-1104,2 8 10 13,13.34,80.90%,12.56,5.95
-1105,6 7 7 10,13.34,73%,10.83,5.3
-1106,2 7 10 11,13.37,78.90%,12.25,6.04
-1107,3 5 7 8,13.37,85.40%,12.95,5.94
-1108,6 8 11 12,13.39,77.90%,12.23,5.74
-1109,1 3 6 10,13.46,82.10%,12.08,6.87
-1110,4 10 10 12,13.46,78.40%,12.16,6.28
-1111,7 10 11 13,13.49,73.30%,11.07,5.79
-1112,4 5 8 10,13.55,75.90%,11.31,5.9
-1113,3 8 9 11,13.57,80.10%,12.64,6.8
-1114,6 12 12 12,13.66,76.60%,11.63,6.62
-1115,2 10 12 13,13.77,81.40%,13.06,6.04
-1116,6 7 10 10,13.83,70.80%,10.47,5.73
-1117,2 3 7 8,13.89,80.20%,12.68,5.59
-1118,3 5 9 9,13.9,79.40%,12.9,6.13
-1119,2 2 5 9,13.93,74.30%,11.71,6.28
-1120,2 4 7 9,13.95,80.60%,13.14,6.36
-1121,2 3 8 9,14.01,81.30%,12.85,6.23
-1122,3 5 5 11,14.19,79%,13.09,5.43
-1123,3 3 9 11,14.2,79.10%,13.05,5.65
-1124,5 7 9 11,14.2,71.10%,11.09,5.89
-1125,1 3 7 9,14.22,80.40%,12.74,5.47
-1126,2 4 9 10,14.24,79.30%,12.99,6
-1127,3 5 12 12,14.26,67.20%,9.62,5.75
-1128,3 3 8 10,14.27,80.30%,13.49,5.53
-1129,4 4 5 13,14.32,73.30%,11.87,7.01
-1130,3 4 4 11,14.39,76.40%,12.57,6.37
-1131,4 5 8 11,14.4,73.80%,11.71,6.59
-1132,3 4 4 10,14.42,77.10%,12.1,6.66
-1133,2 6 12 13,14.46,78.60%,13.39,5.8
-1134,3 6 11 13,14.46,73.20%,11.3,6.74
-1135,1 3 5 6,14.49,86.30%,14.16,5.91
-1136,3 3 4 13,14.49,76.80%,12.26,6.76
-1137,4 6 9 12,14.54,77.80%,12.61,7.02
-1138,6 10 10 13,14.57,69.10%,10.81,5.99
-1139,2 5 9 10,14.58,78.10%,13.05,6.18
-1140,5 5 9 11,14.6,71.10%,12.27,5.16
-1141,3 4 7 12,14.65,82.40%,14.04,6.09
-1142,2 6 9 12,14.66,82.70%,13.71,6.54
-1143,5 6 6 12,14.72,80.70%,13.46,6.01
-1144,2 5 9 11,14.75,76.50%,13.08,6.84
-1145,5 9 9 12,14.78,75.70%,12.97,6.67
-1146,4 4 4 10,14.82,76.90%,12.75,6.5
-1147,3 4 10 10,14.86,75.50%,12.9,6.06
-1148,2 2 5 10,14.91,78.60%,13.45,5.78
-1149,3 3 9 10,14.94,78.10%,13.6,6.1
-1150,4 7 8 12,14.95,79.40%,13.86,6.69
-1151,7 9 12 12,14.96,64.20%,9.28,5.41
-1152,2 2 7 7,14.97,69.80%,11.21,5.96
-1153,3 6 8 13,14.97,81.30%,14.28,6.12
-1154,2 2 3 4,14.98,77.40%,13.18,6.13
-1155,5 7 8 10,14.99,71.20%,12.15,5.63
-1156,1 5 11 12,15.01,71%,11.36,6.19
-1157,4 6 8 10,15.11,76.70%,12.71,7.59
-1158,2 4 10 11,15.16,79.20%,13.93,6.45
-1159,3 6 6 13,15.17,72.80%,12.06,6.38
-1160,3 6 8 12,15.17,80.50%,13.79,6.15
-1161,1 4 4 6,15.31,79.60%,13.55,6.77
-1162,2 5 8 8,15.42,71%,11.68,6.59
-1163,7 7 7 12,15.42,65.20%,8.98,6.11
-1164,4 5 7 13,15.43,72.80%,12.42,6.22
-1165,5 5 5 12,15.48,67.40%,10.23,6.42
-1166,2 6 11 12,15.49,77.70%,13.96,6.35
-1167,2 3 8 10,15.61,82.20%,14.94,6.54
-1168,1 5 5 10,15.65,71.20%,11.64,6.98
-1169,2 5 5 9,15.7,78.50%,14.39,5.98
-1170,5 7 12 12,15.74,66%,10.44,6.26
-1171,4 7 8 11,15.79,69%,10.78,6.62
-1172,3 3 8 13,15.87,69.70%,11.39,6.25
-1173,1 3 12 13,16.02,75.60%,13.66,6.86
-1174,1 3 8 10,16.05,71%,12.31,6.49
-1175,2 3 6 7,16.08,80.80%,15.05,6.62
-1176,2 2 6 7,16.26,72.50%,13.03,6.84
-1177,5 6 9 13,16.28,69%,12.46,6.11
-1178,6 10 11 12,16.3,69.70%,12.94,6.17
-1179,4 9 9 12,16.33,71.80%,13.42,6.47
-1180,2 6 6 9,16.46,72.80%,13.03,6.55
-1181,1 3 11 12,16.48,75.60%,14.03,7.05
-1182,3 10 11 13,16.48,71.20%,13.31,6.3
-1183,1 4 10 12,16.53,72.10%,12.81,6.61
-1184,5 5 6 8,16.54,78.10%,14.9,6.54
-1185,6 8 9 10,16.55,71.20%,12.69,7.07
-1186,2 6 10 11,16.57,73.10%,13.69,6.98
-1187,3 9 9 13,16.62,67.20%,12.08,6.04
-1188,2 4 4 9,16.71,73.40%,13.42,7.28
-1189,10 12 12 12,16.86,64.10%,10.25,5.73
-1190,2 3 7 10,16.91,76.90%,14.96,6.78
-1191,2 7 10 10,17.01,68.10%,13.35,5.81
-1192,8 8 8 11,17.01,63.50%,10.21,5.97
-1193,6 6 9 10,17.09,69.80%,13.35,6.88
-1194,2 3 7 7,17.1,76.50%,15.28,6.63
-1195,6 6 6 10,17.22,66%,11.49,6.49
-1196,5 7 7 10,17.26,68%,13.19,6.49
-1197,2 2 9 12,17.27,72.90%,14.93,6.39
-1198,3 6 8 10,17.4,66.60%,11.97,6.77
-1199,3 5 7 12,17.46,71%,14.01,7.67
-1200,3 5 8 9,17.49,75.80%,15.67,7.53
-1201,3 9 9 11,17.68,70.50%,13.69,7.48
-1202,2 8 10 12,17.74,79.80%,16.35,7.45
-1203,3 8 9 13,17.76,73.40%,15.28,7.09
-1204,9 9 9 12,17.85,63.80%,9.66,6.59
-1205,3 4 8 13,17.98,70%,13.65,7.61
-1206,2 6 7 10,18.01,69.80%,12.96,7.22
-1207,2 6 9 10,18.43,69.70%,14.78,6.85
-1208,4 5 6 9,18.44,75.30%,16.49,7.02
-1209,8 10 12 13,18.47,67.30%,14.31,6.27
-1210,2 6 8 9,18.52,72.40%,15.58,6.65
-1211,3 5 12 13,18.66,67.90%,14.53,6.59
-1212,2 4 7 8,18.98,76.50%,16.76,7.16
-1213,4 8 9 12,18.98,72.50%,16.31,6.84
-1214,3 5 9 10,19.12,68.10%,14.34,7.53
-1215,2 3 10 10,19.18,69.50%,14.5,7.5
-1216,2 2 4 12,19.2,75.10%,15.22,9.15
-1217,2 2 9 10,19.3,65.30%,14.24,7.34
-1218,2 7 10 12,19.38,70.20%,15.86,7.05
-1219,2 2 4 4,19.5,72%,15.01,8.18
-1220,2 3 11 11,19.52,68.30%,14.88,7.67
-1221,1 3 3 8,19.57,74%,15.7,7.86
-1222,6 8 8 9,19.59,63%,11.71,7.2
-1223,8 10 12 12,19.59,63.60%,11.62,7.37
-1224,10 10 10 12,19.93,61.50%,10.24,6.8
-1225,2 3 9 12,20.01,67.30%,13.12,7.61
-1226,6 8 10 11,20.09,64.60%,13.61,7.17
-1227,7 8 8 10,20.1,61.20%,12.07,7.63
-1228,1 5 7 10,20.39,69.50%,16.27,7.63
-1229,3 5 10 11,20.53,69.50%,15.99,8.37
-1230,5 9 12 13,20.53,64.40%,15.67,7.03
-1231,1 3 8 11,20.54,73.40%,16.85,8.28
-1232,2 7 12 12,20.66,62.80%,12.25,7.39
-1233,1 2 8 10,20.88,67.70%,15.12,7.81
-1234,9 11 12 12,20.88,59.50%,10.72,6.43
-1235,1 6 10 12,21.11,65.80%,13.99,7.82
-1236,4 4 8 9,21.14,69.60%,16.8,8.28
-1237,2 3 3 5,21.16,69.50%,15.7,8.49
-1238,3 6 9 13,21.16,68.90%,17.22,7.59
-1239,3 3 7 8,21.37,68.30%,16.85,8.09
-1240,3 5 7 11,21.39,64%,14.47,7.8
-1241,2 3 5 8,21.44,72.90%,17.88,7.71
-1242,4 5 7 10,21.65,63.60%,14.31,7.8
-1243,5 7 10 13,22.5,61.60%,16.46,7.35
-1244,2 9 10 10,22.59,60.60%,16.58,7.36
-1245,2 4 5 10,22.9,67%,17.3,8.07
-1246,2 3 6 8,23.06,75.30%,20.16,8.88
-1247,5 9 11 13,23.25,61.50%,16.69,7.75
-1248,3 4 5 13,23.39,65.60%,17.76,8.49
-1249,1 5 9 12,23.75,62.60%,14.09,8.74
-1250,3 4 4 4,24.47,67.30%,17.82,9.81
-1251,5 7 9 10,24.53,61%,15.63,8.68
-1252,6 6 7 11,24.57,58%,13.11,8.12
-1253,2 5 10 12,24.67,63.70%,17.88,8.55
-1254,4 10 11 12,24.71,62.90%,17.56,8.97
-1255,2 4 5 5,24.72,60.40%,15.23,8.46
-1256,2 2 5 8,24.84,63.50%,16.69,9.19
-1257,8 8 9 12,24.93,56.70%,12.65,7.52
-1258,3 4 7 8,25.04,61.10%,14.33,8.25
-1259,5 6 6 9,25.04,59.20%,14.12,8.75
-1260,3 5 10 10,25.12,54.30%,12.34,6.59
-1261,2 2 6 11,25.22,62.20%,18.4,8.58
-1262,4 9 10 12,25.27,61.70%,17.05,9.01
-1263,8 9 11 11,25.64,58%,15.82,8.41
-1264,3 9 11 13,26.26,57.60%,13.9,8.31
-1265,4 7 8 10,26.26,62.90%,18.82,9.21
-1266,5 6 7 9,26.31,60.10%,16.12,9.07
-1267,2 6 9 9,26.53,58%,14.29,8.07
-1268,2 3 13 13,26.93,58.40%,17.55,9.57
-1269,1 7 12 13,27.75,56.30%,15.1,9.28
-1270,1 4 9 13,27.76,61.10%,18.83,10.02
-1271,4 10 10 11,27.76,54.80%,19.34,9.04
-1272,3 4 5 11,28.16,63.30%,20.54,9.87
-1273,3 3 6 6,28.41,58%,14.18,8.38
-1274,6 6 9 13,28.91,53.80%,13.19,8.3
-1275,4 5 9 12,29.11,60.90%,22.06,10.07
-1276,2 3 7 9,30.64,57.60%,18.33,10.12
-1277,4 4 7 13,30.78,53.80%,15.04,9.44
-1278,2 2 7 10,30.9,53.40%,16.2,8.61
-1279,2 5 12 13,31.29,58%,22.35,9.97
-1280,1 4 6 10,31.39,56.30%,18.81,9.37
-1281,3 6 7 10,31.92,57.40%,21.85,10.37
-1282,1 2 7 7,32.73,55%,17.28,10.37
-1283,2 5 8 12,32.82,54.60%,17.78,9.75
-1284,3 8 10 12,34.45,53.60%,20.48,10.33
-1285,6 7 11 11,35.1,46.40%,19.8,10.15
-1286,2 2 3 10,35.18,54.90%,18.21,10.27
-1287,2 5 7 9,35.29,52.70%,23.49,10.35
-1288,2 4 7 7,35.73,50.70%,17.22,9.67
-1289,2 2 6 9,36.11,52.10%,17.94,10.05
-1290,7 9 11 12,36.61,50%,17.36,10.44
-1291,4 7 9 11,37.32,51.40%,22.71,11.23
-1292,2 2 3 5,38.49,51.50%,15.34,8.81
-1293,5 5 8 11,39.1,45.80%,24.18,10.92
-1294,3 6 6 10,39.12,50.30%,18.48,10.7
-1295,7 8 9 12,41.23,48.70%,16.03,10.12
-1296,1 5 11 11,41.41,43.30%,15.93,9.75
-1297,5 10 10 11,42.21,41.30%,14.82,7.03
-1298,1 2 9 11,45.03,44.90%,22.27,12.16
-1299,1 5 5 5,45.49,43.60%,11.92,7.27
-1300,4 9 11 11,47.16,43.30%,25.26,13.98
-1301,5 9 10 11,47.17,42.70%,28.12,13.4
-1302,1 7 13 13,47.38,41.70%,13.99,8.41
-1303,6 7 7 11,48.6,42.40%,27.65,14.51
-1304,3 3 7 7,48.84,41.20%,12.6,7.37
-1305,5 10 10 13,49.61,38.50%,17.09,8.06
-1306,3 5 8 13,50.53,41.90%,30.48,13.89
-1307,8 9 10 12,51.24,42.50%,21.95,13.98
-1308,2 5 6 9,52.95,44%,27.17,13.41
-1309,1 6 11 13,54.62,38.10%,18.47,9.98
-1310,1 3 9 10,55.16,40.80%,23.19,12.28
-1311,4 6 6 10,55.98,40.20%,22.83,12.31
-1312,4 4 7 7,56.41,38.80%,14.56,8.11
-1313,2 2 11 11,57.22,40.10%,13.2,8.62
-1314,5 7 9 12,57.59,39.60%,17.8,11.41
-1315,4 5 6 12,58.44,44.10%,21.82,16.05
-1316,4 6 7 9,58.49,42%,19.47,12.15
-1317,6 12 12 13,60.5,37.90%,15.11,8.3
-1318,4 4 10 10,60.61,39.70%,13.47,9.13
-1319,4 7 11 13,61.01,37%,30.84,16.48
-1320,4 5 6 11,61.48,38%,19.42,10.88
-1321,3 5 8 12,62.47,38%,27.97,13.34
-1322,3 4 6 13,63.1,40.40%,20.37,11.69
-1323,6 7 8 12,63.27,40.70%,16.9,12.18
-1324,2 4 11 12,63.58,39.40%,23.37,13.12
-1325,5 7 10 11,64.03,35.50%,23.76,13.12
-1326,2 2 13 13,64.44,37.30%,14.05,8.25
-1327,6 11 12 12,64.75,36.30%,15.4,8.93
-1328,5 8 9 13,64.8,34.90%,34.56,16.2
-1329,7 10 12 13,67.86,37.40%,22.3,12.55
-1330,5 6 8 13,68.54,36.40%,22.65,11.42
-1331,2 7 8 9,69.74,39.80%,18.28,10.87
-1332,2 2 6 13,71.49,33.10%,30.83,16.35
-1333,5 6 9 11,72.21,33.40%,26.15,15
-1334,1 4 7 11,72.93,35.70%,29.78,16.43
-1335,6 9 9 10,80.98,31.60%,32.75,18.6
-1336,2 5 7 8,83.96,32.90%,28.52,15.64
-1337,7 8 8 13,84.91,29.50%,27.08,15.03
-1338,5 7 7 11,88.29,27.40%,25.62,12.84
-1339,3 3 7 13,88.82,27.60%,32.8,18.62
-1340,4 8 8 13,88.89,29.10%,24.53,14.05
-1341,4 8 8 11,88.95,30%,23.19,13.84
-1342,3 8 8 10,90.4,28.90%,25.35,16.08
-1343,2 7 7 10,90.8,27%,26.85,12.93
-1344,5 5 7 11,91.1,26.10%,25.79,13.15
-1345,2 3 8 13,92.19,32%,23.64,12.74
-1346,7 8 10 11,95.22,29.80%,24.05,14.09
-1347,2 2 10 11,96.37,30.20%,26.2,17.05
-1348,9 11 12 13,99.38,27.90%,22.06,13.19
-1349,1 6 6 8,101.21,29.80%,21.64,13.19
-1350,3 3 8 8,104.24,30.80%,15.65,10.6
-1351,1 8 12 12,104.58,29%,20.38,13.12
-1352,3 3 5 7,104.76,29.20%,28.32,17.86
-1353,2 4 7 12,108.41,28.10%,28.97,16.35
-1354,2 9 13 13,110.09,24.90%,30.9,19.5
-1355,3 7 9 13,110.86,26%,39.84,21.67
-1356,2 4 10 10,111.72,25.80%,28.72,18.53
-1357,3 6 6 11,112.28,27.10%,24.73,15.29
-1358,3 5 7 13,122.99,23.60%,38.68,21.2
-1359,2 5 5 10,188.2,22.10%,24.09,16.42
-1360,1 4 5 6,195.91,28.70%,18.38,11.81
-1361,1 3 4 6,201.57,26.90%,19.77,12.1
+Rank,Puzzles,AMT (s),Solved rate,1-sigma Mean (s),1-sigma STD (s)
+1,1 1 4 6,4.4,99.20%,4.67,1.48
+2,1 1 11 11,4.41,99.60%,4.68,1.45
+3,1 1 3 8,4.45,99.20%,4.69,1.48
+4,1 1 1 8,4.48,98.80%,4.66,1.25
+5,6 6 6 6,4.59,99.40%,4.82,1.49
+6,1 1 2 12,4.63,99.10%,4.95,1.57
+7,1 2 2 6,4.8,99.20%,5.16,1.61
+8,1 1 10 12,4.81,99%,5.06,1.54
+9,2 2 10 10,4.85,98.20%,5.13,1.63
+10,1 1 1 12,4.86,99.20%,5.18,1.63
+11,1 1 2 8,4.96,97.80%,5.31,1.76
+12,1 1 4 8,4.99,98.10%,5.35,1.83
+13,1 1 5 8,5,96.40%,5.36,1.81
+14,4 6 11 11,5,98.90%,5.38,1.73
+15,1 1 3 12,5.02,97.30%,5.36,1.84
+16,2 2 2 12,5.02,99.10%,5.37,1.7
+17,1 1 4 12,5.03,97.40%,5.45,1.85
+18,1 1 12 12,5.03,99.40%,5.4,1.66
+19,3 3 3 8,5.04,98%,5.4,1.8
+20,1 1 2 6,5.09,98.40%,5.4,1.61
+21,1 1 2 11,5.09,98.90%,5.41,1.7
+22,1 2 3 4,5.1,99%,5.46,1.85
+23,11 11 12 12,5.1,98.70%,5.5,1.87
+24,3 7 7 8,5.11,96.70%,5.48,1.89
+25,1 1 13 13,5.16,99.30%,5.45,1.66
+26,1 2 4 12,5.16,97.60%,5.56,1.99
+27,1 1 3 6,5.17,97.70%,5.52,1.77
+28,1 1 3 9,5.17,97%,5.52,1.85
+29,7 7 12 12,5.17,98.60%,5.58,1.89
+30,4 6 7 7,5.18,98.60%,5.53,1.83
+31,1 1 2 13,5.19,98.70%,5.49,1.78
+32,1 1 5 6,5.19,96.80%,5.62,2.04
+33,1 1 11 13,5.23,99.10%,5.66,1.85
+34,1 6 6 12,5.24,98.70%,5.57,1.72
+35,4 5 12 12,5.24,97%,5.6,1.79
+36,4 6 13 13,5.24,98.70%,5.63,1.79
+37,12 12 12 12,5.25,99.30%,5.73,2.14
+38,2 11 11 12,5.26,97.20%,5.68,2
+39,4 4 4 6,5.26,98.10%,5.61,1.94
+40,1 1 1 11,5.27,97.30%,5.56,1.66
+41,1 1 11 12,5.27,99.30%,5.67,1.8
+42,2 7 7 12,5.29,98.70%,5.65,1.89
+43,1 5 7 12,5.3,98.30%,5.87,2.12
+44,10 10 12 12,5.31,98.60%,5.74,1.91
+45,1 8 8 8,5.32,97.70%,5.65,1.94
+46,2 2 3 8,5.33,98.10%,5.72,1.93
+47,2 9 9 12,5.33,97.30%,5.74,2
+48,11 11 11 12,5.33,94.80%,5.61,1.99
+49,3 8 13 13,5.34,94.80%,5.68,2.06
+50,9 9 12 12,5.34,98.30%,5.84,2.11
+51,1 1 5 5,5.35,99.10%,5.71,1.8
+52,3 3 12 12,5.36,98.70%,5.83,2.03
+53,1 1 4 5,5.38,97.80%,5.77,2.02
+54,1 6 8 12,5.38,96.80%,5.77,2
+55,8 8 12 12,5.38,98.50%,5.8,1.95
+56,3 8 11 11,5.39,96%,5.74,2.09
+57,5 6 12 12,5.39,97.10%,5.78,1.81
+58,11 12 12 12,5.4,97.60%,5.74,1.88
+59,12 12 13 13,5.4,98.60%,5.79,1.91
+60,1 1 12 13,5.42,98.70%,5.8,1.84
+61,1 3 5 12,5.42,96.40%,5.79,2.1
+62,5 5 12 12,5.42,98.70%,5.78,1.75
+63,1 9 9 12,5.44,95.50%,5.72,1.78
+64,2 3 3 12,5.44,98.60%,5.91,2.04
+65,3 4 4 8,5.44,98.30%,5.85,1.94
+66,3 8 10 10,5.44,95.60%,5.89,2.25
+67,3 8 9 9,5.45,97%,5.83,1.98
+68,2 5 5 12,5.46,98.70%,5.87,1.99
+69,11 11 11 13,5.48,98.40%,6.03,2.26
+70,2 12 13 13,5.49,97.10%,5.85,2.04
+71,7 7 11 12,5.49,93.70%,5.78,2.05
+72,1 1 3 7,5.5,96.80%,5.82,1.95
+73,1 4 10 10,5.5,97.90%,5.91,2.1
+74,4 4 12 12,5.51,98.60%,5.93,2.05
+75,1 3 4 12,5.52,98.50%,5.95,1.97
+76,5 5 11 12,5.52,96.50%,5.89,1.9
+77,1 2 5 8,5.54,95.50%,5.94,2.26
+78,2 2 4 6,5.54,99%,5.92,1.9
+79,1 6 7 12,5.56,98.50%,5.97,1.97
+80,1 8 9 12,5.56,96.50%,5.87,1.96
+81,6 7 12 12,5.56,95.60%,5.93,2.03
+82,1 3 10 10,5.57,97.40%,5.87,2
+83,2 3 3 8,5.57,96.90%,5.94,1.94
+84,3 5 5 8,5.57,96.90%,5.97,2.08
+85,1 1 1 13,5.58,97.40%,5.9,1.77
+86,2 3 12 12,5.58,97.40%,5.97,2
+87,1 4 7 12,5.59,98.30%,5.99,2.02
+88,8 8 11 13,5.6,97%,6.1,2.29
+89,1 3 3 4,5.61,97.40%,5.86,1.79
+90,1 8 8 12,5.61,96.30%,5.91,1.9
+91,3 7 8 8,5.63,95.90%,5.99,2.03
+92,7 8 12 12,5.63,96.10%,6.07,2.12
+93,9 9 11 12,5.63,93.90%,5.98,2.15
+94,1 2 5 12,5.64,95.40%,6.09,2.2
+95,2 7 7 8,5.64,96.80%,5.94,1.99
+96,4 4 11 13,5.64,97.90%,6.25,2.36
+97,1 1 4 7,5.65,97.10%,5.95,2.07
+98,1 1 10 13,5.65,97.90%,6.09,2.2
+99,4 6 6 6,5.65,98.30%,6.02,2.07
+100,5 5 7 7,5.65,97.80%,5.97,1.91
+101,4 5 11 12,5.66,97.40%,6.06,2.09
+102,1 6 9 12,5.67,95.40%,6.05,2.13
+103,1 7 9 12,5.68,95.90%,6.08,2.24
+104,2 2 12 12,5.68,98.70%,6.21,2.26
+105,1 1 2 10,5.69,95.60%,5.96,1.81
+106,3 7 7 7,5.69,97.40%,6.03,2.02
+107,1 7 7 10,5.7,97.80%,6.17,2.18
+108,4 5 5 6,5.7,97.30%,6.11,2.11
+109,4 8 11 11,5.7,95.10%,6.01,1.98
+110,1 2 2 10,5.71,96.60%,6.05,2.11
+111,1 6 6 11,5.71,98.20%,6.18,2.24
+112,3 3 4 6,5.71,97.70%,6.14,2.11
+113,4 4 11 12,5.71,91.90%,6.06,2.35
+114,2 2 2 3,5.72,99%,5.98,1.72
+115,3 3 11 12,5.72,94.70%,6.11,2.19
+116,3 6 11 11,5.72,94.40%,5.99,2.05
+117,3 8 8 8,5.73,97.50%,6.09,2.14
+118,12 12 12 13,5.73,97.40%,6.07,1.98
+119,1 2 2 12,5.74,97.70%,6.1,1.99
+120,2 8 9 9,5.74,96.30%,6.13,2.1
+121,6 6 12 12,5.75,98.40%,6.31,2.35
+122,8 9 12 12,5.75,96.20%,6.26,2.24
+123,1 5 8 8,5.76,94.80%,6.35,2.69
+124,2 2 3 12,5.76,98%,6.17,2.02
+125,3 4 4 6,5.77,97.40%,6.1,1.93
+126,1 5 6 12,5.79,98.50%,6.17,2.07
+127,4 6 9 9,5.79,96.30%,6.17,2.22
+128,6 6 11 13,5.79,97.80%,6.3,2.25
+129,7 7 11 13,5.79,97.40%,6.32,2.29
+130,1 4 4 12,5.81,95.60%,6.31,2.36
+131,1 4 6 12,5.81,96.60%,6.23,2.41
+132,9 9 11 13,5.81,96.80%,6.35,2.41
+133,3 8 12 13,5.82,96%,6.19,2.2
+134,1 8 10 12,5.83,95.60%,6.16,2.19
+135,3 4 11 13,5.84,96.40%,6.32,2.35
+136,11 13 13 13,5.85,97.80%,6.41,2.42
+137,4 6 6 7,5.87,96.40%,6.23,2.1
+138,1 3 6 12,5.88,96.80%,6.27,2.18
+139,2 4 4 12,5.88,98.40%,6.24,2.14
+140,3 6 7 7,5.88,93.30%,6.24,2.33
+141,4 6 8 8,5.88,96.80%,6.31,2.23
+142,1 4 5 12,5.89,95.70%,6.33,2.33
+143,10 11 12 12,5.89,95.90%,6.27,2.05
+144,2 4 11 11,5.9,96.20%,6.33,2.34
+145,4 6 10 11,5.9,94.50%,6.3,2.39
+146,4 8 10 10,5.9,97.50%,6.32,2.15
+147,1 4 8 12,5.91,97.50%,6.26,2.2
+148,2 10 10 12,5.91,97%,6.29,2.31
+149,3 6 13 13,5.91,94.50%,6.15,1.95
+150,5 6 11 12,5.91,97.20%,6.36,2.23
+151,6 6 10 13,5.91,93.70%,6.43,2.68
+152,10 10 11 13,5.91,97.60%,6.49,2.37
+153,1 3 3 12,5.92,95.30%,6.32,2.31
+154,1 7 8 9,5.92,97.10%,6.38,2.27
+155,1 12 13 13,5.92,94.30%,6.25,2.17
+156,3 8 10 11,5.92,94.40%,6.27,2.35
+157,9 10 12 12,5.92,95.70%,6.24,2.02
+158,1 11 11 12,5.93,93.40%,6.17,2.22
+159,4 5 12 13,5.93,97.60%,6.42,2.16
+160,1 3 6 8,5.94,95.40%,6.37,2.53
+161,1 2 11 11,5.95,97.30%,6.44,2.44
+162,1 6 12 12,5.95,94.50%,6.37,2.45
+163,2 8 8 12,5.95,97.40%,6.47,2.44
+164,3 6 6 8,5.95,97%,6.35,2.24
+165,5 5 5 5,5.95,95.20%,6.36,2.16
+166,3 7 10 10,5.96,97.30%,6.35,2.19
+167,2 8 10 10,5.97,95.30%,6.28,2.07
+168,4 6 10 10,5.97,96.30%,6.34,2.37
+169,1 1 3 13,5.98,95.10%,6.36,2.13
+170,1 2 6 6,5.98,97.40%,6.41,2.25
+171,1 6 7 10,5.98,98.50%,6.53,2.26
+172,1 2 4 4,5.99,97.10%,6.33,2.14
+173,1 7 8 10,5.99,97.50%,6.41,2.3
+174,4 5 5 5,5.99,97.40%,6.55,2.42
+175,1 1 4 4,6.01,94.50%,6.31,2.43
+176,1 2 6 12,6.01,96.80%,6.38,2.24
+177,1 5 8 12,6.02,98.10%,6.51,2.23
+178,1 2 10 12,6.03,97.40%,6.55,2.43
+179,1 5 7 11,6.03,98.50%,6.48,2.14
+180,1 10 10 12,6.03,92.30%,6.24,2.1
+181,3 8 12 12,6.03,96.50%,6.47,2.46
+182,2 2 11 13,6.04,98%,6.59,2.43
+183,3 9 9 9,6.04,97.60%,6.47,2.16
+184,1 1 6 6,6.05,97.20%,6.46,2.19
+185,1 2 4 8,6.05,96.50%,6.44,2.31
+186,1 6 8 9,6.05,98.20%,6.5,2.36
+187,3 4 12 12,6.05,94.50%,6.52,2.48
+188,4 8 13 13,6.05,94.40%,6.27,1.91
+189,1 1 3 4,6.08,98%,6.4,2.03
+190,1 3 9 12,6.08,96.20%,6.48,2.34
+191,3 8 11 12,6.08,94.20%,6.55,2.64
+192,1 1 9 13,6.09,97.20%,6.55,2.54
+193,1 2 6 8,6.09,95.90%,6.41,2.33
+194,1 7 8 8,6.09,96.60%,6.61,2.66
+195,2 8 13 13,6.09,90.20%,6.39,2.56
+196,2 11 11 11,6.09,90%,6.21,2.47
+197,2 11 12 12,6.09,95.40%,6.49,2.27
+198,4 7 7 8,6.09,93.80%,6.55,2.52
+199,3 3 4 8,6.1,96.20%,6.43,2.14
+200,2 2 11 12,6.11,92.30%,6.5,2.56
+201,5 6 12 13,6.11,97.40%,6.51,2.17
+202,3 3 11 13,6.12,96.90%,6.62,2.5
+203,4 7 12 12,6.12,88.50%,6.51,2.85
+204,2 13 13 13,6.14,91.10%,6.32,2.32
+205,5 5 12 13,6.14,95.60%,6.62,2.14
+206,10 13 13 13,6.14,93.20%,6.58,2.78
+207,1 7 7 12,6.15,88.90%,6.28,2.32
+208,2 3 11 12,6.15,94.80%,6.74,2.75
+209,5 5 11 13,6.15,96.10%,6.75,2.52
+210,1 2 3 10,6.16,98.40%,6.47,2
+211,1 5 5 12,6.16,90.50%,6.36,2.32
+212,1 5 7 13,6.16,97.90%,6.53,2.09
+213,3 3 3 6,6.17,97.70%,6.71,2.56
+214,3 3 3 12,6.17,96.90%,6.65,2.46
+215,10 10 11 12,6.17,93.30%,6.53,2.29
+216,1 4 7 8,6.19,96.60%,6.68,2.71
+217,1 10 12 12,6.19,95.80%,6.81,2.94
+218,8 8 11 12,6.19,90.60%,6.54,2.66
+219,1 2 3 8,6.2,97%,6.61,2.38
+220,4 4 10 13,6.2,93.50%,6.71,2.79
+221,1 4 6 8,6.21,94.40%,6.57,2.52
+222,2 2 4 5,6.21,98.30%,6.6,2.11
+223,11 12 13 13,6.21,95.10%,6.69,2.43
+224,1 2 2 8,6.22,93.30%,6.65,2.63
+225,5 5 5 6,6.22,96.10%,6.68,2.34
+226,1 1 5 7,6.23,96.40%,6.56,2.09
+227,1 2 13 13,6.23,97.20%,6.93,2.69
+228,7 7 12 13,6.23,93.90%,6.63,2.35
+229,1 4 8 8,6.24,96.50%,6.71,2.48
+230,3 11 11 12,6.24,91.40%,6.44,2.17
+231,1 2 3 12,6.25,96.20%,6.8,2.53
+232,1 3 8 9,6.27,92.20%,6.82,3.18
+233,1 3 11 11,6.27,97.20%,6.68,2.43
+234,1 4 4 5,6.27,96.90%,6.82,2.67
+235,1 5 10 10,6.28,97.30%,6.65,2.3
+236,1 8 10 13,6.28,94.80%,6.6,2.43
+237,2 2 2 8,6.28,96.40%,6.84,2.73
+238,1 4 12 12,6.29,91.20%,6.53,2.49
+239,2 7 8 12,6.29,94.90%,6.73,2.56
+240,4 4 5 6,6.29,95.70%,6.6,2.26
+241,7 9 11 11,6.29,95.80%,6.77,2.64
+242,2 12 12 13,6.3,95.50%,6.69,2.37
+243,6 7 11 12,6.3,96.80%,6.84,2.59
+244,4 6 12 13,6.31,93.80%,6.82,2.88
+245,1 2 10 11,6.32,97.60%,6.66,2.39
+246,1 5 8 9,6.32,93.90%,6.76,2.62
+247,1 5 7 8,6.33,94.70%,6.71,2.42
+248,4 8 9 9,6.33,90.40%,6.49,2.28
+249,9 9 12 13,6.33,94%,6.69,2.25
+250,8 8 8 10,6.35,90.40%,6.7,2.91
+251,2 12 12 12,6.36,98.40%,6.75,2.46
+252,1 8 8 9,6.37,96.30%,6.9,2.71
+253,2 8 11 11,6.37,88.50%,6.55,2.78
+254,4 5 13 13,6.37,87.70%,6.61,2.77
+255,1 2 12 12,6.38,95.80%,6.71,2.2
+256,2 8 10 11,6.38,94.70%,6.87,2.62
+257,2 11 13 13,6.38,94.10%,6.88,2.52
+258,1 6 7 11,6.39,97.40%,7.02,2.89
+259,3 6 7 8,6.39,97.80%,6.79,2.31
+260,1 2 8 8,6.4,96.10%,6.97,2.74
+261,4 5 9 9,6.4,88.40%,6.64,2.72
+262,1 3 3 7,6.41,95.90%,7.25,3.33
+263,2 5 6 12,6.41,95.10%,6.8,2.52
+264,3 6 10 11,6.41,95.80%,7.05,2.86
+265,1 3 6 6,6.42,97.20%,6.89,2.58
+266,1 6 6 13,6.42,97.20%,6.8,2.44
+267,4 4 4 4,6.43,97.80%,7.03,2.93
+268,10 10 10 13,6.43,92.90%,6.86,2.99
+269,1 6 9 9,6.44,97.50%,6.87,2.52
+270,2 6 6 12,6.44,97.70%,6.97,2.55
+271,3 7 9 9,6.44,91%,6.87,2.78
+272,1 7 8 12,6.45,95.60%,6.97,2.67
+273,3 12 13 13,6.46,91%,6.62,2.29
+274,6 6 11 12,6.46,88.90%,6.77,2.97
+275,1 3 5 8,6.47,95.10%,7.13,2.89
+276,6 10 10 10,6.47,95.70%,6.74,2.31
+277,2 3 11 13,6.48,91.90%,6.91,2.78
+278,1 1 3 11,6.49,94.30%,6.73,1.97
+279,1 4 4 7,6.49,96.10%,7.22,3.18
+280,1 2 4 6,6.5,95.90%,6.88,2.53
+281,1 9 12 12,6.5,92.60%,6.86,2.82
+282,3 4 5 8,6.5,96.10%,6.96,2.63
+283,4 5 6 6,6.5,95.30%,6.81,2.33
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+1258,3 4 7 8,25.04,61.10%,14.33,8.25
+1259,5 6 6 9,25.04,59.20%,14.12,8.75
+1260,3 5 10 10,25.12,54.30%,12.34,6.59
+1261,2 2 6 11,25.22,62.20%,18.4,8.58
+1262,4 9 10 12,25.27,61.70%,17.05,9.01
+1263,8 9 11 11,25.64,58%,15.82,8.41
+1264,3 9 11 13,26.26,57.60%,13.9,8.31
+1265,4 7 8 10,26.26,62.90%,18.82,9.21
+1266,5 6 7 9,26.31,60.10%,16.12,9.07
+1267,2 6 9 9,26.53,58%,14.29,8.07
+1268,2 3 13 13,26.93,58.40%,17.55,9.57
+1269,1 7 12 13,27.75,56.30%,15.1,9.28
+1270,1 4 9 13,27.76,61.10%,18.83,10.02
+1271,4 10 10 11,27.76,54.80%,19.34,9.04
+1272,3 4 5 11,28.16,63.30%,20.54,9.87
+1273,3 3 6 6,28.41,58%,14.18,8.38
+1274,6 6 9 13,28.91,53.80%,13.19,8.3
+1275,4 5 9 12,29.11,60.90%,22.06,10.07
+1276,2 3 7 9,30.64,57.60%,18.33,10.12
+1277,4 4 7 13,30.78,53.80%,15.04,9.44
+1278,2 2 7 10,30.9,53.40%,16.2,8.61
+1279,2 5 12 13,31.29,58%,22.35,9.97
+1280,1 4 6 10,31.39,56.30%,18.81,9.37
+1281,3 6 7 10,31.92,57.40%,21.85,10.37
+1282,1 2 7 7,32.73,55%,17.28,10.37
+1283,2 5 8 12,32.82,54.60%,17.78,9.75
+1284,3 8 10 12,34.45,53.60%,20.48,10.33
+1285,6 7 11 11,35.1,46.40%,19.8,10.15
+1286,2 2 3 10,35.18,54.90%,18.21,10.27
+1287,2 5 7 9,35.29,52.70%,23.49,10.35
+1288,2 4 7 7,35.73,50.70%,17.22,9.67
+1289,2 2 6 9,36.11,52.10%,17.94,10.05
+1290,7 9 11 12,36.61,50%,17.36,10.44
+1291,4 7 9 11,37.32,51.40%,22.71,11.23
+1292,2 2 3 5,38.49,51.50%,15.34,8.81
+1293,5 5 8 11,39.1,45.80%,24.18,10.92
+1294,3 6 6 10,39.12,50.30%,18.48,10.7
+1295,7 8 9 12,41.23,48.70%,16.03,10.12
+1296,1 5 11 11,41.41,43.30%,15.93,9.75
+1297,5 10 10 11,42.21,41.30%,14.82,7.03
+1298,1 2 9 11,45.03,44.90%,22.27,12.16
+1299,1 5 5 5,45.49,43.60%,11.92,7.27
+1300,4 9 11 11,47.16,43.30%,25.26,13.98
+1301,5 9 10 11,47.17,42.70%,28.12,13.4
+1302,1 7 13 13,47.38,41.70%,13.99,8.41
+1303,6 7 7 11,48.6,42.40%,27.65,14.51
+1304,3 3 7 7,48.84,41.20%,12.6,7.37
+1305,5 10 10 13,49.61,38.50%,17.09,8.06
+1306,3 5 8 13,50.53,41.90%,30.48,13.89
+1307,8 9 10 12,51.24,42.50%,21.95,13.98
+1308,2 5 6 9,52.95,44%,27.17,13.41
+1309,1 6 11 13,54.62,38.10%,18.47,9.98
+1310,1 3 9 10,55.16,40.80%,23.19,12.28
+1311,4 6 6 10,55.98,40.20%,22.83,12.31
+1312,4 4 7 7,56.41,38.80%,14.56,8.11
+1313,2 2 11 11,57.22,40.10%,13.2,8.62
+1314,5 7 9 12,57.59,39.60%,17.8,11.41
+1315,4 5 6 12,58.44,44.10%,21.82,16.05
+1316,4 6 7 9,58.49,42%,19.47,12.15
+1317,6 12 12 13,60.5,37.90%,15.11,8.3
+1318,4 4 10 10,60.61,39.70%,13.47,9.13
+1319,4 7 11 13,61.01,37%,30.84,16.48
+1320,4 5 6 11,61.48,38%,19.42,10.88
+1321,3 5 8 12,62.47,38%,27.97,13.34
+1322,3 4 6 13,63.1,40.40%,20.37,11.69
+1323,6 7 8 12,63.27,40.70%,16.9,12.18
+1324,2 4 11 12,63.58,39.40%,23.37,13.12
+1325,5 7 10 11,64.03,35.50%,23.76,13.12
+1326,2 2 13 13,64.44,37.30%,14.05,8.25
+1327,6 11 12 12,64.75,36.30%,15.4,8.93
+1328,5 8 9 13,64.8,34.90%,34.56,16.2
+1329,7 10 12 13,67.86,37.40%,22.3,12.55
+1330,5 6 8 13,68.54,36.40%,22.65,11.42
+1331,2 7 8 9,69.74,39.80%,18.28,10.87
+1332,2 2 6 13,71.49,33.10%,30.83,16.35
+1333,5 6 9 11,72.21,33.40%,26.15,15
+1334,1 4 7 11,72.93,35.70%,29.78,16.43
+1335,6 9 9 10,80.98,31.60%,32.75,18.6
+1336,2 5 7 8,83.96,32.90%,28.52,15.64
+1337,7 8 8 13,84.91,29.50%,27.08,15.03
+1338,5 7 7 11,88.29,27.40%,25.62,12.84
+1339,3 3 7 13,88.82,27.60%,32.8,18.62
+1340,4 8 8 13,88.89,29.10%,24.53,14.05
+1341,4 8 8 11,88.95,30%,23.19,13.84
+1342,3 8 8 10,90.4,28.90%,25.35,16.08
+1343,2 7 7 10,90.8,27%,26.85,12.93
+1344,5 5 7 11,91.1,26.10%,25.79,13.15
+1345,2 3 8 13,92.19,32%,23.64,12.74
+1346,7 8 10 11,95.22,29.80%,24.05,14.09
+1347,2 2 10 11,96.37,30.20%,26.2,17.05
+1348,9 11 12 13,99.38,27.90%,22.06,13.19
+1349,1 6 6 8,101.21,29.80%,21.64,13.19
+1350,3 3 8 8,104.24,30.80%,15.65,10.6
+1351,1 8 12 12,104.58,29%,20.38,13.12
+1352,3 3 5 7,104.76,29.20%,28.32,17.86
+1353,2 4 7 12,108.41,28.10%,28.97,16.35
+1354,2 9 13 13,110.09,24.90%,30.9,19.5
+1355,3 7 9 13,110.86,26%,39.84,21.67
+1356,2 4 10 10,111.72,25.80%,28.72,18.53
+1357,3 6 6 11,112.28,27.10%,24.73,15.29
+1358,3 5 7 13,122.99,23.60%,38.68,21.2
+1359,2 5 5 10,188.2,22.10%,24.09,16.42
+1360,1 4 5 6,195.91,28.70%,18.38,11.81
+1361,1 3 4 6,201.57,26.90%,19.77,12.1
 1362,2 3 5 12,207.89,20.70%,29.96,22.37
\ No newline at end of file
diff --git a/data/crosswords/mini0505.json b/src/tot/data/crosswords/mini0505.json
similarity index 100%
rename from data/crosswords/mini0505.json
rename to src/tot/data/crosswords/mini0505.json
diff --git a/data/crosswords/mini0505_0_100_5.json b/src/tot/data/crosswords/mini0505_0_100_5.json
similarity index 100%
rename from data/crosswords/mini0505_0_100_5.json
rename to src/tot/data/crosswords/mini0505_0_100_5.json
diff --git a/data/text/data_100_random_text.txt b/src/tot/data/text/data_100_random_text.txt
similarity index 100%
rename from data/text/data_100_random_text.txt
rename to src/tot/data/text/data_100_random_text.txt
diff --git a/src/tot/methods/bfs.py b/src/tot/methods/bfs.py
new file mode 100644
index 0000000..4675bb9
--- /dev/null
+++ b/src/tot/methods/bfs.py
@@ -0,0 +1,96 @@
+import itertools
+import numpy as np
+from functools import partial
+from tot.models import gpt
+
+def get_value(task, x, y, n_evaluate_sample, cache_value=True):
+    value_prompt = task.value_prompt_wrap(x, y)
+    if cache_value and value_prompt in task.value_cache:
+        return task.value_cache[value_prompt]
+    value_outputs = gpt(value_prompt, n=n_evaluate_sample, stop=None)
+    value = task.value_outputs_unwrap(x, y, value_outputs)
+    if cache_value:
+        task.value_cache[value_prompt] = value
+    return value
+
+def get_values(task, x, ys, n_evaluate_sample, cache_value=True):
+    values = []
+    local_value_cache = {}
+    for y in ys:  # each partial output
+        if y in local_value_cache:  # avoid duplicate candidates
+            value = 0
+        else:    
+            value = get_value(task, x, y, n_evaluate_sample, cache_value=cache_value)
+            local_value_cache[y] = value
+        values.append(value)
+    return values
+
+def get_votes(task, x, ys, n_evaluate_sample):
+    vote_prompt = task.vote_prompt_wrap(x, ys)
+    vote_outputs = gpt(vote_prompt, n=n_evaluate_sample, stop=None)
+    values = task.vote_outputs_unwrap(vote_outputs, len(ys))
+    return values
+
+def get_proposals(task, x, y): 
+    propose_prompt = task.propose_prompt_wrap(x, y)
+    proposals = gpt(propose_prompt, n=1, stop=None)[0].split('\n')
+    return [y + _ + '\n' for _ in proposals]
+
+def get_samples(task, x, y, n_generate_sample, prompt_sample, stop):
+    if prompt_sample == 'standard':
+        prompt = task.standard_prompt_wrap(x, y)
+    elif prompt_sample == 'cot':
+        prompt = task.cot_prompt_wrap(x, y)
+    else:
+        raise ValueError(f'prompt_sample {prompt_sample} not recognized')
+    samples = gpt(prompt, n=n_generate_sample, stop=stop)
+    return [y + _ for _ in samples]
+
+def solve(args, task, idx, to_print=True):
+    global gpt
+    gpt = partial(gpt, model=args.backend, temperature=args.temperature)
+    print(gpt)
+    x = task.get_input(idx)  # input
+    ys = ['']  # current output candidates
+    infos = []
+    for step in range(task.steps):
+        # generation
+        if args.method_generate == 'sample':
+            new_ys = [get_samples(task, x, y, args.n_generate_sample, prompt_sample=args.prompt_sample, stop=task.stops[step]) for y in ys]
+        elif args.method_generate == 'propose':
+            new_ys = [get_proposals(task, x, y) for y in ys]
+        new_ys = list(itertools.chain(*new_ys))
+        ids = list(range(len(new_ys)))
+        # evaluation
+        if args.method_evaluate == 'vote':
+            values = get_votes(task, x, new_ys, args.n_evaluate_sample)
+        elif args.method_evaluate == 'value':
+            values = get_values(task, x, new_ys, args.n_evaluate_sample)
+
+        # selection
+        if args.method_select == 'sample':
+            ps = np.array(values) / sum(values)
+            select_ids = np.random.choice(ids, size=args.n_select_sample, p=ps).tolist()
+        elif args.method_select == 'greedy':
+            select_ids = sorted(ids, key=lambda x: values[x], reverse=True)[:args.n_select_sample]
+        select_new_ys = [new_ys[select_id] for select_id in select_ids]
+
+        # log
+        if to_print: 
+            sorted_new_ys, sorted_values = zip(*sorted(zip(new_ys, values), key=lambda x: x[1], reverse=True))
+            print(f'-- new_ys --: {sorted_new_ys}\n-- sol values --: {sorted_values}\n-- choices --: {select_new_ys}\n')
+        
+        infos.append({'step': step, 'x': x, 'ys': ys, 'new_ys': new_ys, 'values': values, 'select_new_ys': select_new_ys})
+        ys = select_new_ys
+    
+    if to_print: 
+        print(ys)
+    return ys, {'steps': infos}
+
+def naive_solve(args, task, idx, to_print=True):
+    global gpt
+    gpt = partial(gpt, model=args.backend, temperature=args.temperature)
+    print(gpt)
+    x = task.get_input(idx)  # input
+    ys = get_samples(task, x, '', args.n_generate_sample, args.prompt_sample, stop=None)
+    return ys, {}
\ No newline at end of file
diff --git a/models.py b/src/tot/models.py
similarity index 95%
rename from models.py
rename to src/tot/models.py
index 7188d9e..05710d6 100644
--- a/models.py
+++ b/src/tot/models.py
@@ -41,5 +41,5 @@ def gpt_usage(backend="gpt-4"):
     if backend == "gpt-4":
         cost = completion_tokens / 1000 * 0.06 + prompt_tokens / 1000 * 0.03
     elif backend == "gpt-3.5-turbo":
-        cost = completion_tokens / 1000 * 0.002 + prompt_tokens / 1000 * 0.0015
+        cost = (completion_tokens + prompt_tokens) / 1000 * 0.0002
     return {"completion_tokens": completion_tokens, "prompt_tokens": prompt_tokens, "cost": cost}
\ No newline at end of file
diff --git a/prompts/crosswords.py b/src/tot/prompts/crosswords.py
similarity index 100%
rename from prompts/crosswords.py
rename to src/tot/prompts/crosswords.py
diff --git a/prompts/game24.py b/src/tot/prompts/game24.py
similarity index 100%
rename from prompts/game24.py
rename to src/tot/prompts/game24.py
diff --git a/prompts/text.py b/src/tot/prompts/text.py
similarity index 100%
rename from prompts/text.py
rename to src/tot/prompts/text.py
diff --git a/src/tot/tasks/__init__.py b/src/tot/tasks/__init__.py
new file mode 100644
index 0000000..11d1cb7
--- /dev/null
+++ b/src/tot/tasks/__init__.py
@@ -0,0 +1,12 @@
+def get_task(name):
+    if name == 'game24':
+        from tot.tasks.game24 import Game24Task
+        return Game24Task()
+    elif name == 'text':
+        from tot.tasks.text import TextTask
+        return TextTask()
+    elif name == 'crosswords':
+        from tot.tasks.crosswords import MiniCrosswordsTask
+        return MiniCrosswordsTask()
+    else:
+        raise NotImplementedError
\ No newline at end of file
diff --git a/tasks/base.py b/src/tot/tasks/base.py
similarity index 73%
rename from tasks/base.py
rename to src/tot/tasks/base.py
index f336145..7624d14 100644
--- a/tasks/base.py
+++ b/src/tot/tasks/base.py
@@ -1,4 +1,5 @@
-DATA_PATH = './data'
+import os
+DATA_PATH = os.path.join(os.path.dirname(__file__), '..', 'data')
 
 class Task:
     def __init__(self):
diff --git a/tasks/crosswords.py b/src/tot/tasks/crosswords.py
similarity index 98%
rename from tasks/crosswords.py
rename to src/tot/tasks/crosswords.py
index 85d2bf0..fb270d0 100644
--- a/tasks/crosswords.py
+++ b/src/tot/tasks/crosswords.py
@@ -1,13 +1,14 @@
 import re
-import json
 import os
-from tasks.base import Task, DATA_PATH
-from prompts.crosswords import * 
-from models import gpt
+import json
+from tot.tasks.base import Task, DATA_PATH
+from tot.prompts.crosswords import * 
+from tot.models import gpt
 
 class MiniCrosswordsEnv:
     def __init__(self, file='mini0505.json'):
-        self.file = f'data/crosswords/{file}'
+        self.file = os.path.join(DATA_PATH, 'crosswords', file)
+
         self.file = json.load(open(self.file))
         self.n = len(self.file)
         self.cache = {}
diff --git a/tasks/game24.py b/src/tot/tasks/game24.py
similarity index 97%
rename from tasks/game24.py
rename to src/tot/tasks/game24.py
index 64182e6..c58f090 100644
--- a/tasks/game24.py
+++ b/src/tot/tasks/game24.py
@@ -2,8 +2,8 @@ import re
 import os
 import sympy
 import pandas as pd
-from tasks.base import Task, DATA_PATH
-from prompts.game24 import * 
+from tot.tasks.base import Task, DATA_PATH
+from tot.prompts.game24 import * 
 
 
 def get_current_numbers(y: str) -> str:
diff --git a/tasks/text.py b/src/tot/tasks/text.py
similarity index 97%
rename from tasks/text.py
rename to src/tot/tasks/text.py
index 52bb417..9d62be8 100644
--- a/tasks/text.py
+++ b/src/tot/tasks/text.py
@@ -1,8 +1,8 @@
 import os
 import re
-from tasks.base import Task, DATA_PATH
-from prompts.text import *
-from models import gpt
+from tot.tasks.base import Task, DATA_PATH
+from tot.prompts.text import *
+from tot.models import gpt
 
 
 class TextTask(Task):
diff --git a/tasks/__init__.py b/tasks/__init__.py
deleted file mode 100644
index 4689ae3..0000000
--- a/tasks/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-def get_task(name, file=None):
-    if name == 'game24':
-        from .game24 import Game24Task
-        return Game24Task(file)
-    elif name == 'text':
-        from .text import TextTask
-        return TextTask(file)
-    elif name == 'crosswords':
-        from .crosswords import MiniCrosswordsTask
-        return MiniCrosswordsTask(file)
-    else:
-        raise NotImplementedError
\ No newline at end of file

From 23629f6da3f2a23a55a2a573f23a158713801dff Mon Sep 17 00:00:00 2001
From: ysymyth <shunyuyao.cs@gmail.com>
Date: Mon, 3 Jul 2023 22:29:31 -0400
Subject: [PATCH 2/5] update readme.md

---
 readme.md | 66 +++++++++++++++++++++++++++++++++++--------------------
 1 file changed, 42 insertions(+), 24 deletions(-)

diff --git a/readme.md b/readme.md
index c16839c..e2be1cc 100644
--- a/readme.md
+++ b/readme.md
@@ -2,44 +2,49 @@
 [![DOI](https://zenodo.org/badge/642099326.svg)](https://zenodo.org/badge/latestdoi/642099326)
 
 <details>
-  <summary>Note: https://github.com/kyegomez/tree-of-thoughts is NOT the correct implementation to replicate paper results. </summary>
-  
+  <summary>Note: https://github.com/kyegomez/tree-of-thoughts CANNOT replicate paper results. </summary>
+
 In fact, people have reported that his code [cannot](https://github.com/kyegomez/tree-of-thoughts/issues/52) [properly](https://github.com/kyegomez/tree-of-thoughts/issues/41) [run](https://github.com/kyegomez/tree-of-thoughts/issues/60), and is [probably automatically generated by ChatGPT](pics/fake.png), and [kyegomez has done so for other popular ML methods](https://twitter.com/qbitium/status/1663954096741814272), while intentionally refusing to link to official implementations for his own interests (see https://github.com/kyegomez/tree-of-thoughts/issues/54, https://github.com/kyegomez/tree-of-thoughts/issues/55, https://github.com/kyegomez/tree-of-thoughts/issues/56).
 Unfortunately, Google/Github searches go to kyegomez's malicious repo by default as it has more stars. **Please DE-STAR his repo and STAR this to help other people avoid being misled, thanks!**
 </details>
 
 
-
-
-
 ![teaser](pics/teaser.png)
 
 Official implementation for paper [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://arxiv.org/abs/2305.10601) with code, prompts, model outputs.
 Also check [its tweet thread](https://twitter.com/ShunyuYao12/status/1659357547474681857) in 1min.
 
 
-Please cite the paper and star this repo if you use ToT and find it interesting/useful. Thanks!
 
-```bibtex
-@misc{yao2023tree,
-      title={{Tree of Thoughts}: Deliberate Problem Solving with Large Language Models}, 
-      author={Shunyu Yao and Dian Yu and Jeffrey Zhao and Izhak Shafran and Thomas L. Griffiths and Yuan Cao and Karthik Narasimhan},
-      year={2023},
-      eprint={2305.10601},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-```
 
 
 
 ## Setup
-You need to first have an OpenAI API key and store it in the environment variable ``OPENAI_API_KEY`` (see [here](https://help.openai.com/en/articles/5112595-best-practices-for-api-key-safety)). If you use custom base url, set it by environment variable ``OPENAI_API_BASE`` (e.g. https://api.openai.com/v1).
+- Set up OpenAI API key and store in environment variable ``OPENAI_API_KEY`` (see [here](https://help.openai.com/en/articles/5112595-best-practices-for-api-key-safety)). 
 
-Package requirement: ``pip install openai backoff sympy numpy``
+- Install dependencies and `tot` package: 
+```bash
+pip install -r requirements.txt
+pip install -e .  # install `tot` package
+```
 
 
-## Experiments
+## Quick Start
+The following minimal script will attempt to solve the game of 24 with `4 5 6 10`:
+```python
+import argparse
+from tot.methods.bfs import solve
+from tot.tasks.game24 import Game24Task
+
+args = argparse.Namespace(backend='gpt-4', temperature=0.7, task='game24', naive_run=False, prompt_sample=None, method_generate='propose', method_evaluate='value', method_select='greedy', n_generate_sample=1, n_evaluate_sample=3, n_select_sample=5)
+
+task = Game24Task()
+
+solve(args, task, 900)
+```
+
+
+## Paper Experiments
 
 Run experiments via ``sh scripts/{game24, text, crosswords}/{standard_sampling, cot_sampling, bfs}.sh``, except in crosswords we use a DFS algorithm for ToT, which can be run via ``scripts/crosswords/search_crosswords-dfs.ipynb``.
 
@@ -55,13 +60,26 @@ The very simple ``run.py`` implements the ToT + BFS algorithm, as well as the na
 
 
 
-## Trajectories
+## Paper Trajectories
 ``logs/`` contains all the trajectories from the paper's experiments, except for ``logs/game24/gpt-4_0.7_propose1_value3_greedy5_start900_end1000.json`` which was reproduced after the paper (as the original experiment was done in a notebook) and achieved a 69\% score instead of the original 74\% score due to randomness in GPT decoding. We hope to aggregate multiple runs in the future to account for sampling randomness and update the paper, but this shouldn't affect the main conclusions of the paper.
 
-
-
-## Questions
-Feel free to contact shunyuyao.cs@gmail.com or open an issue if you have any questions.
+## How to Add New Tasks/Methods
+TBA.
 
 
 
+
+
+## Citations
+Please cite the paper and star this repo if you use ToT and find it interesting/useful, thanks! Feel free to contact shunyuyao.cs@gmail.com or open an issue if you have any questions.
+
+```bibtex
+@misc{yao2023tree,
+      title={{Tree of Thoughts}: Deliberate Problem Solving with Large Language Models}, 
+      author={Shunyu Yao and Dian Yu and Jeffrey Zhao and Izhak Shafran and Thomas L. Griffiths and Yuan Cao and Karthik Narasimhan},
+      year={2023},
+      eprint={2305.10601},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
\ No newline at end of file

From 066015df5618803b78c47926001527152cc2bcf9 Mon Sep 17 00:00:00 2001
From: ysymyth <shunyuyao.cs@gmail.com>
Date: Mon, 3 Jul 2023 22:39:33 -0400
Subject: [PATCH 3/5] update

---
 readme.md | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/readme.md b/readme.md
index e2be1cc..ba30c7d 100644
--- a/readme.md
+++ b/readme.md
@@ -39,10 +39,17 @@ from tot.tasks.game24 import Game24Task
 args = argparse.Namespace(backend='gpt-4', temperature=0.7, task='game24', naive_run=False, prompt_sample=None, method_generate='propose', method_evaluate='value', method_select='greedy', n_generate_sample=1, n_evaluate_sample=3, n_select_sample=5)
 
 task = Game24Task()
-
-solve(args, task, 900)
+ys, infos = solve(args, task, 900)
+print(ys[0])
 ```
 
+And the output would be something like (note it's not deterministic, and sometimes the output can be wrong):
+```
+10 - 4 = 6 (left: 5 6 6)
+5 * 6 = 30 (left: 6 30)
+30 - 6 = 24 (left: 24)
+Answer: (5 * (10 - 4)) - 6 = 24
+```
 
 ## Paper Experiments
 

From 57adb30df794119592305cec7075bc6c28874a45 Mon Sep 17 00:00:00 2001
From: ysymyth <shunyuyao.cs@gmail.com>
Date: Tue, 4 Jul 2023 22:51:19 -0400
Subject: [PATCH 4/5] update

---
 readme.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/readme.md b/readme.md
index ba30c7d..5811d87 100644
--- a/readme.md
+++ b/readme.md
@@ -24,13 +24,15 @@ Also check [its tweet thread](https://twitter.com/ShunyuYao12/status/16593575474
 
 - Install dependencies and `tot` package: 
 ```bash
+git clone https://github.com/princeton-nlp/tree-of-thought-llm
+cd tree-of-thought-llm
 pip install -r requirements.txt
 pip install -e .  # install `tot` package
 ```
 
 
 ## Quick Start
-The following minimal script will attempt to solve the game of 24 with `4 5 6 10`:
+The following minimal script will attempt to solve the game of 24 with `4 5 6 10` (might be a bit slow as it's using GPT-4):
 ```python
 import argparse
 from tot.methods.bfs import solve
@@ -75,8 +77,6 @@ TBA.
 
 
 
-
-
 ## Citations
 Please cite the paper and star this repo if you use ToT and find it interesting/useful, thanks! Feel free to contact shunyuyao.cs@gmail.com or open an issue if you have any questions.
 

From 43f174039144d2a8ce18492a57cf5e4e526ed9ad Mon Sep 17 00:00:00 2001
From: ysymyth <shunyuyao.cs@gmail.com>
Date: Tue, 4 Jul 2023 22:57:03 -0400
Subject: [PATCH 5/5] update

---
 readme.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/readme.md b/readme.md
index 5811d87..7165c26 100644
--- a/readme.md
+++ b/readme.md
@@ -22,7 +22,7 @@ Also check [its tweet thread](https://twitter.com/ShunyuYao12/status/16593575474
 ## Setup
 - Set up OpenAI API key and store in environment variable ``OPENAI_API_KEY`` (see [here](https://help.openai.com/en/articles/5112595-best-practices-for-api-key-safety)). 
 
-- Install dependencies and `tot` package: 
+- Install dependencies and `tot` package (PyPI package coming soon): 
 ```bash
 git clone https://github.com/princeton-nlp/tree-of-thought-llm
 cd tree-of-thought-llm
@@ -72,10 +72,10 @@ The very simple ``run.py`` implements the ToT + BFS algorithm, as well as the na
 ## Paper Trajectories
 ``logs/`` contains all the trajectories from the paper's experiments, except for ``logs/game24/gpt-4_0.7_propose1_value3_greedy5_start900_end1000.json`` which was reproduced after the paper (as the original experiment was done in a notebook) and achieved a 69\% score instead of the original 74\% score due to randomness in GPT decoding. We hope to aggregate multiple runs in the future to account for sampling randomness and update the paper, but this shouldn't affect the main conclusions of the paper.
 
-## How to Add New Tasks/Methods
-TBA.
-
-
+## How to Add A New Task
+Setting up a new task is easy, and mainly involves two steps.
+* Set up a new task class in ``tot/tasks/`` and task files in ``tot/data/``. See ``tot/tasks/game24.py`` for an example. Add the task to ``tot/tasks/__init__.py``.
+* Set up task-specific prompts in ``tot/prompts/``. See ``tot/prompts/game24.py`` for an example. Depending on the nature of the task, choose ``--method_generate`` (choices=[``sample``, ``propose``]) and ``--method_evaluate`` (choices=[``value``, ``vote``]) and their corresponding prompts. 
 
 ## Citations
 Please cite the paper and star this repo if you use ToT and find it interesting/useful, thanks! Feel free to contact shunyuyao.cs@gmail.com or open an issue if you have any questions.