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] 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