diff --git a/examples/arenahard_pipeline.py b/examples/arenahard_pipeline.py new file mode 100644 index 0000000..189f8ca --- /dev/null +++ b/examples/arenahard_pipeline.py @@ -0,0 +1,71 @@ +from automation.pipelines import Pipeline +from automation.tasks import ArenaHardGenerateTask, ArenaHardJudgeTask + + +step1 = ArenaHardGenerateTask( + project_name="alexandre_debug", + task_name="generate_task", + generate_model="Qwen/Qwen2.5-1.5B-Instruct", + rate_type="throughput", + backend="aiohttp_server", + GUIDELLM__MAX_CONCURRENCY=256, + GUIDELLM__REQUEST_TIMEOUT=21600, + target="http://localhost:8000/v1", + max_seconds=30, + data="prompt_tokens=128,output_tokens=128", + branch = "arena_upgrade", + #vllm_kwargs={"enable-chunked-prefill": True} + + generation_config_file='gen_answer_config.yaml', + generation_endpoint_file='api_config.yaml', +) + +step1.create_task() + + +step2 = ArenaHardJudgeTask( + project_name="alexandre_debug", + task_name="judge_task", + answer_task_id = "cf688bf523c842ff8d8c9d721613aabc", + judgement_model="Qwen/Qwen2.5-1.5B-Instruct", + rate_type="throughput", + backend="aiohttp_server", + GUIDELLM__MAX_CONCURRENCY=256, + GUIDELLM__REQUEST_TIMEOUT=21600, + target="http://localhost:8000/v1", + max_seconds=30, + data="prompt_tokens=128,output_tokens=128", + branch = "arena_upgrade", + #vllm_kwargs={"enable-chunked-prefill": True} + + judgement_setting_file='arena-hard-v2.0.yaml', + judgement_endpoint_file='api_config.yaml', +) + +step2.create_task() + + +pipeline = Pipeline( + project_name="alexandre_debug", + pipeline_name="pipeline_arenahard", +) + + +pipeline.add_step( + name="pipeline_arenahard_generate_step1", + base_task_id = step1.id, + execution_queue="remote-upgrade-default", + #monitor_models=[step1.get_arguments()["Args"]["save_directory"]], + #monitor_artifacts=["recipe"], +) + +pipeline.add_step( + name="pipeline_arenahard_judgement_step2", + base_task_id = step2.id, + parents=["pipeline_arenahard_generate_step1"], + execution_queue="remote-upgrade-default", + #parameter_override={"Args/model_id": "${pipeline_arenahard_generate_step1.models.output.-1.id}"}, + #monitor_metrics=[("gsm8k", "exact_match,strict-match")], +) + +pipeline.execute_remotely() diff --git a/examples/generate_arenahard_example.py b/examples/generate_arenahard_example.py new file mode 100644 index 0000000..759648d --- /dev/null +++ b/examples/generate_arenahard_example.py @@ -0,0 +1,24 @@ +from automation.tasks import ArenaHardGenerateTask + +task = ArenaHardGenerateTask( + project_name="simple_debug", + task_name="generate_math_task_4", + #generate_model="meta-llama/Llama-3.2-1B-Instruct", + #generate_model="Qwen/Qwen2.5-1.5B-Instruct", + generate_model="Qwen/Qwen2.5-Math-1.5B-Instruct", + rate_type="throughput", + backend="aiohttp_server", + target="http://localhost:8000/v1", + max_seconds=30, + data="prompt_tokens=128,output_tokens=128", + branch = "arena_upgrade", + #vllm_kwargs={"enable-chunked-prefill": True} + + #generation_config_file='gen_answer_config.yaml', + generation_config_file='math_answer_config.yaml', + #generation_endpoint_file='api_config.yaml', + generation_endpoint_file='math_api_config.yaml', +) + +task.execute_remotely("oneshot-a100x4") +#task.execute_locally() diff --git a/examples/guidellm_example.py b/examples/guidellm_example.py index f09b1e6..fe2e297 100644 --- a/examples/guidellm_example.py +++ b/examples/guidellm_example.py @@ -9,11 +9,12 @@ GUIDELLM__MAX_CONCURRENCY=256, GUIDELLM__REQUEST_TIMEOUT=21600, target="http://localhost:8000/v1", - data_type="emulated", max_seconds=30, - data="prompt_tokens=512,generated_tokens=256", + #scenario = "benchmarking_32k", + data="prompt_tokens=128,output_tokens=128", + branch = "update_guidellm", vllm_kwargs={"enable-chunked-prefill": True} ) task.execute_remotely("oneshot-a100x1") -#task.execute_locally() \ No newline at end of file +#task.execute_locally() diff --git a/examples/judge_arenahard_example.py b/examples/judge_arenahard_example.py new file mode 100644 index 0000000..11a63f3 --- /dev/null +++ b/examples/judge_arenahard_example.py @@ -0,0 +1,27 @@ +from automation.tasks import ArenaHardJudgeTask + +task = ArenaHardJudgeTask( + project_name="alexandre_debug", + task_name="test_judge_task", + #answer_task_id = "cf688bf523c842ff8d8c9d721613aabc", + #answer_task_id = "4630730469114ed397fc876d578a469e", + #judgement_model="meta-llama/Llama-3.2-1B-Instruct", + #judgement_model="Qwen/Qwen2.5-1.5B-Instruct", + judgement_model="Qwen/Qwen2.5-Math-1.5B-Instruct", + rate_type="throughput", + backend="aiohttp_server", + target="http://localhost:8000/v1", + max_seconds=30, + data="prompt_tokens=128,output_tokens=128", + branch = "arena_upgrade", + #vllm_kwargs={"enable-chunked-prefill": True} + + #judgement_setting_file='arena-hard-v2.0.yaml', + judgement_setting_file='math-arena-hard-v2.0.yaml', + #judgement_endpoint_file='api_config.yaml', + judgement_endpoint_file ='math_api_config.yaml', +) + +#task.execute_remotely("oneshot-a100x1") +task.execute_remotely("remote-upgrade-default") +#task.execute_locally() diff --git a/examples/lmeval_example.py b/examples/lmeval_example.py index 8910aa2..688c355 100644 --- a/examples/lmeval_example.py +++ b/examples/lmeval_example.py @@ -6,8 +6,8 @@ model_id="meta-llama/Llama-3.2-1B-Instruct", tasks="gsm8k", model_args="dtype=auto,max_model_len=8192", - batch_size="auto", + batch_size="auto", ) task.execute_remotely("oneshot-a100x1") -#task.execute_locally() \ No newline at end of file +#task.execute_locally() diff --git a/src/automation/configs.py b/src/automation/configs.py index 76dbe58..ac6e92c 100644 --- a/src/automation/configs.py +++ b/src/automation/configs.py @@ -1,2 +1,4 @@ -DEFAULT_DOCKER_IMAGE = "498127099666.dkr.ecr.us-east-1.amazonaws.com/mlops/k8s-research-cuda12_5:latest" -DEFAULT_OUTPUT_URI = "gs://neuralmagic-clearml" \ No newline at end of file +DEFAULT_DOCKER_IMAGE = "quay.io/nmmlops/mlops/k8s-research-cuda12_8:latest" +DEFAULT_OUTPUT_URI = "gs://neuralmagic-clearml" +DEFAULT_RESEARCH_BRANCH = "main" +DEFAULT_GUIDELLM_SCENARIO = "chat" diff --git a/src/automation/standards/arenahard/api_config.yaml b/src/automation/standards/arenahard/api_config.yaml new file mode 100644 index 0000000..d37a321 --- /dev/null +++ b/src/automation/standards/arenahard/api_config.yaml @@ -0,0 +1,10 @@ +qwen2.5-1.5b-instruct: + model: Qwen/Qwen2.5-1.5B-Instruct + endpoints: + - api_base: http://127.0.0.1:8000/v1 + api_key: '-' + api_type: openai + temperature: 0.6 + end_think_token: "" + max_tokens: 20000 + parallel: 1 diff --git a/src/automation/standards/arenahard/arena-hard-v2.0.yaml b/src/automation/standards/arenahard/arena-hard-v2.0.yaml new file mode 100644 index 0000000..7e86633 --- /dev/null +++ b/src/automation/standards/arenahard/arena-hard-v2.0.yaml @@ -0,0 +1,16 @@ +judge_model: qwen2.5-1.5b-instruct +temperature: 0.0 +max_tokens: 20000 + +bench_name: arena-hard-v2.0 + +reference: null + +regex_patterns: + - \[\[([AB<>=]+)\]\] + - \[([AB<>=]+)\] + +prompt_template: "<|User Prompt|>\n{QUESTION}\n\n<|The Start of Assistant A's Answer|>\n{ANSWER_A}\n<|The End of Assistant A's Answer|>\n\n<|The Start of Assistant B's Answer|>\n{ANSWER_B}\n<|The End of Assistant B's Answer|>" + +model_list: + - qwen2.5-1.5b-instruct diff --git a/src/automation/standards/arenahard/arena-hard-v2.0/model_answer/o3-mini-2025-01-31.jsonl b/src/automation/standards/arenahard/arena-hard-v2.0/model_answer/o3-mini-2025-01-31.jsonl new file mode 100644 index 0000000..8a62dff --- /dev/null +++ b/src/automation/standards/arenahard/arena-hard-v2.0/model_answer/o3-mini-2025-01-31.jsonl @@ -0,0 +1,750 @@ +{"uid":"0253f09025cc40cd","ans_id":"dgYCTvj3bz42JVPxnkZNB2","model":"o3-mini-2025-01-31","messages":[{"role":"user","content":"A banking company is successfully operating its public mobile banking stack on AWS. The mobile banking stack is deployed in a VPC that includes private subnets and public subnets. The company is using IPv4 networking and has not deployed or supported IPv6 in the environment. The company has decided to adopt a third-party service provider's API and must integrate the API with the existing environment. The service provider’s API requires the use of IPv6.\nA network engineer must turn on IPv6 connectivity for the existing workload that is deployed in a private subnet. The company does not want to permit IPv6 traffic from the public internet and mandates that the company's servers must initiate all IPv6 connectivity. The network engineer turns on IPv6 in the VPC and in the private subnets.\nWhich solution will meet these requirements?\n\nA. Create an internet gateway and a NAT gateway in the VPC. Add a route to the existing subnet route tables to point IPv6 traffic to the NAT gateway.\nB. Create an internet gateway and a NAT instance in the VPC. Add a route to the existing subnet route tables to point IPv6 traffic to the NAT instance.\nC. Create an egress-only Internet gateway in the VPAdd a route to the existing subnet route tables to point IPv6 traffic to the egress-only internet gateway.\nD. Create an egress-only internet gateway in the VPC. Configure a security group that denies all inbound traffic. Associate the security group with the egress-only internet gateway."},{"role":"assistant","content":{"answer":"The correct answer is C.\n\nAn egress-only internet gateway for IPv6 allows outbound communication from instances in your VPC to the internet, while preventing unsolicited inbound traffic from the internet. This meets the requirement that the company's servers must initiate all IPv6 connectivity and that no IPv6 traffic from the public internet is allowed to enter.\n\nOption A and Option B describe NAT solutions, which are applicable for IPv4. AWS does not support NAT for IPv6 since IPv6 addresses are globally unique, and NAT is not generally needed for IPv6 traffic. Option D incorrectly suggests that you can associate a security group with an egress-only internet gateway, but security groups are not applicable to internet gateways.\n\nTherefore, by creating an egress-only internet gateway in the VPC and adding a route to the private subnet route tables that directs IPv6 traffic to it (Option C), the requirement will be fulfilled."}}],"tstamp":1744155079.1383833885,"metadata":{"token_len":179,"header_count":{"h1":0,"h2":0,"h3":0,"h4":0,"h5":0,"h6":0},"list_count":{"ordered":0,"unordered":0},"bold_count":{"**":0,"__":0}}} +{"uid":"0275cb6eae1649c1","ans_id":"H7vFkLc9mEYWdctawjxGfa","model":"o3-mini-2025-01-31","messages":[{"role":"user","content":"To start with, our own perceptual treatment of colorfulness involves the difference between Red and Green and the difference between Blue and Yellow:\n\na ~ R-G\nb ~ B - (R + G)\/2\n\nor something like that\n\nbut the process above suggests a more symmetrical and three-dimensional notion of colorfulness that isn’t grounded in perception:\n\na’ ~ R - (G + B)\nb’ ~ G - (R + B)\nc’ ~ B - (R + G)\n\nso my question really was more, how would you take such a more perceptually grounded version with only two dimensions and map it over these three dimensions to get the “perceptual equivalent” of what the math tells us for this infinite saturation compression limit."},{"role":"assistant","content":{"answer":"One common conclusion is that although a “fully symmetric” treatment of the three color channels leads to three numbers (for instance, using\n\n  a′ = R – (G + B)\n  b′ = G – (R + B)\n  c′ = B – (R + G)),\n\nthe human visual system “sees” only two independent chromatic differences, namely the red–green and blue–yellow directions. In our perceptual models we often use\n\n  a = R – G    (red–green axis)\n  b = B – (R + G)\/2 (blue–yellow axis)\n\nand it turns out that these two are directly obtainable by suitable linear combinations (i.e. projections) of the three symmetric quantities. For example, note that\n\n  a′ – b′ = [R – (G+B)] – [G – (R+B)]\n       = R – G – B – G + R + B\n       = 2(R – G).\n\nThus, if we take\n\n  red–green = (a′ – b′)\/2,\n\nwe recover exactly R – G.\n\nSimilarly, observe that\n\n  c′ = B – (R + G)\n     = 2 [B – (R + G)\/2].\n\nSo if we define\n\n  blue–yellow = c′\/2,\n\nwe get exactly B – (R + G)\/2.\n\nThus, a “perceptual equivalent” of the infinite‐saturation (or saturation–compression) limit that starts with the three “symmetric” differences is achieved by projecting onto these two opponent axes. In other words, although our mathematical derivation produces three numbers (a′, b′, c′), they are not independent (in fact, one may show that they are linearly related once you account for the overall brightness). The natural mapping to the two perceptual dimensions is\n\n  red–green = (a′ – b′)\/2 and blue–yellow = c′\/2,\n\nwhich is completely equivalent to our 2D perceptually grounded system (R – G and B – (R+G)\/2).\n\nMore generally, if one starts with full RGB space one first “factors out” luminance (often taken to be L = (R+G+B)\/3) so that the remaining degrees of freedom lie in a plane. An appropriate choice of coordinates for this chromatic plane is, for instance, the opponent channels discussed above. Hence, the mathematical scheme that gives three symmetric differences may be “collapsed” into the two perceptually relevant dimensions by a simple linear transformation.\n\nSo, to answer your question succinctly: you take the symmetric (three‐dimensional) differences and project them onto the two opponent axes by forming the combinations\n\n  red–green = (a′ – b′)\/2 \n  blue–yellow = c′\/2,\n\nwhich exactly recovers the perceptual differences R – G and B – (R+G)\/2 that we use to describe human color perception in the saturation–compression limit."}}],"tstamp":1744155130.9595468044,"metadata":{"token_len":654,"header_count":{"h1":0,"h2":0,"h3":0,"h4":0,"h5":0,"h6":0},"list_count":{"ordered":0,"unordered":0},"bold_count":{"**":0,"__":0}}} +{"uid":"03ac130e6ed44aaa","ans_id":"VqDsZJRGsEssYFAGL5pwT6","model":"o3-mini-2025-01-31","messages":[{"role":"user","content":"Create a responsive instagram login page in flutter also including imges and dark theme"},{"role":"assistant","content":{"answer":"Below is a complete Flutter example for a responsive Instagram‐style login page that includes an image (assumed to be an Instagram logo) and supports a dark theme. Be sure to add your image assets (for example, an Instagram logo in this case) and update your pubspec.yaml file accordingly.\n\nStep 1. Create a new Flutter project and add your asset (e.g., “assets\/images\/instagram_logo.png”). Then, update your pubspec.yaml so Flutter knows about the asset:\n\n----------------------------------------------------\n# pubspec.yaml (excerpt)\nflutter:\n assets:\n - assets\/images\/instagram_logo.png\n----------------------------------------------------\n\nStep 2. Replace your lib\/main.dart with the code below:\n\n----------------------------------------------------\nimport 'package:flutter\/material.dart';\n\nvoid main() {\n runApp(MyApp());\n}\n\nclass MyApp extends StatelessWidget {\n @override\n Widget build(BuildContext context) {\n return MaterialApp(\n title: 'Instagram Login',\n \/\/ Define light and dark themes.\n theme: ThemeData(\n \/\/ Light theme settings.\n brightness: Brightness.light,\n primarySwatch: Colors.blue,\n inputDecorationTheme: InputDecorationTheme(\n border: OutlineInputBorder(),\n ),\n ),\n darkTheme: ThemeData(\n \/\/ Dark theme settings.\n brightness: Brightness.dark,\n primarySwatch: Colors.blue,\n inputDecorationTheme: InputDecorationTheme(\n border: OutlineInputBorder(),\n ),\n ),\n themeMode: ThemeMode.system, \/\/ Automatically switch based on system theme.\n home: LoginPage(),\n );\n }\n}\n\nclass LoginPage extends StatelessWidget {\n @override\n Widget build(BuildContext context) {\n final screenSize = MediaQuery.of(context).size;\n\n return Scaffold(\n \/\/ A responsive SingleChildScrollView helps on small screens (e.g., when the keyboard is shown).\n body: SingleChildScrollView(\n child: ConstrainedBox(\n constraints: BoxConstraints(minHeight: screenSize.height),\n child: Center(\n child: Padding(\n padding: const EdgeInsets.all(16.0),\n child: Column(\n \/\/ Center the login form vertically.\n mainAxisAlignment: MainAxisAlignment.center,\n children: [\n \/\/ The Instagram logo image.\n \/\/ Adjust the width and height based on screen width.\n Container(\n width: screenSize.width > 600 ? 300 : 200,\n height: screenSize.width > 600 ? 300 : 200,\n child: Image.asset('assets\/images\/instagram_logo.png'),\n ),\n SizedBox(height: 32.0),\n \/\/ Username TextField\n TextField(\n decoration: InputDecoration(\n labelText: 'Username',\n prefixIcon: Icon(Icons.person),\n ),\n ),\n SizedBox(height: 16.0),\n \/\/ Password TextField with obscured text\n TextField(\n obscureText: true,\n decoration: InputDecoration(\n labelText: 'Password',\n prefixIcon: Icon(Icons.lock),\n ),\n ),\n SizedBox(height: 16.0),\n \/\/ Login button stretches to fill the width.\n SizedBox(\n width: double.infinity,\n child: ElevatedButton(\n onPressed: () {\n \/\/ Implement your login logic here.\n },\n child: Text('Log In'),\n ),\n ),\n \/\/ Forgot Password link.\n TextButton(\n onPressed: () {\n \/\/ Implement forgot password logic.\n },\n child: Text('Forgot Password?'),\n ),\n ],\n ),\n ),\n ),\n ),\n ),\n );\n }\n}\n----------------------------------------------------\n\nHow the code works:\n\n1. MaterialApp is configured with both light and dark themes. By setting themeMode to ThemeMode.system, the app automatically picks based on the device settings.\n\n2. The LoginPage widget uses MediaQuery to detect screen size so that it can adjust the logo size for larger screens (for example, a tablet) versus smaller screens (a mobile).\n\n3. A SingleChildScrollView combined with a ConstrainedBox ensures that the page scrolls if the vertical space is limited (e.g., when the keyboard appears).\n\n4. Two TextField widgets are provided for username and password, and they come with appropriate decoration (icon and label). The password field uses obscureText: true.\n\n5. An ElevatedButton allows the user to “Log In” and a TextButton provides a “Forgot Password?” option.\n\nStep 3. Run your app:\n• In your terminal, execute: \n  flutter run\n\nYou now have a simple, responsive Instagram-style login page with dark theme support built in Flutter. Feel free to adjust the styling, spacing, and logic to match your desired functionality."}}],"tstamp":1744155077.9535803795,"metadata":{"token_len":964,"header_count":{"h1":1,"h2":0,"h3":0,"h4":0,"h5":0,"h6":0},"list_count":{"ordered":5,"unordered":1},"bold_count":{"**":0,"__":0}}} +{"uid":"045a786b4e5d4ec6","ans_id":"Tgq2VR2ebCk3xSGYaSo2Vx","model":"o3-mini-2025-01-31","messages":[{"role":"user","content":"uint8_t select_action(uint8_t state) {\n int i;\n if((float)rand() \/ RAND_MAX < EPSILON) {\n return rand() % ACTION_SIZE;\n } else {\n \/\/ Ñ¡Ôñ×î¼Ñ¶¯×÷\n uint8_t best_action = 0;\n float max_q = Q[state][0];\n for(i = 0; i < ACTION_SIZE; i++) {\n if(Q[state][i] >= max_q) {\n max_q = Q[state][i];\n best_action = i;\n }\n }\n return best_action;\n }\n}\n\n\/\/ ?????????\nfloat take_action_and_get_reward(uint8_t action) {\n float reward = 0;\n uint8_t new_state;\n if(action == 0)\n {\n Car_SpinLeft(1500, 1500);\n delay_ms(20);\n }\n else if(action == 1) \/\/ÓÒ´óÍä\n {\n Car_SpinRight(1500, 1500);\n delay_ms(20);\n }\n else if(action == 2)\n {\n Car_Run(3000 \/ 2);\n delay_ms(20);\n }\n\n new_state = get_state();\n\n if(new_state == b1001) {\n reward = 3; \/\/ ?????????????\n } else if((new_state == b1011) || (new_state == b1101)) {\n reward = 0; \/\/ ??????????????\n } else {\n reward = -1; \/\/ ??????????????\n }\n\n return reward;\n}\n\n\/\/ ??Q?\nvoid update_q_value(uint8_t state, uint8_t action, float reward, uint8_t new_state) {\n float max_q = Q[new_state][0];\n int i;\n for(i = 1; i < ACTION_SIZE; i++) {\n if(Q[new_state][i] > max_q) {\n max_q = Q[new_state][i];\n }\n }\n\n Q[state][action] += ALPHA * (reward + GAMMA * max_q - Q[state][action]);\n}\n\n\/\/ ?????\nvoid train(void) {\n double EPSILON = 0.1;\n int episode;\n uint8_t new_state;\n for(episode = 0; episode < 1000; episode++) { \/\/ ????1000???\n uint8_t state = get_state();\n while(1) {\n uint8_t action = select_action(state);\n float reward = take_action_and_get_reward(action);\n new_state = get_state();\n update_q_value(state, action, reward, new_state);\n state = new_state;\n if(new_state == b1111) {\n Car_Stop();\n while(get_state() != b1001);\n delay_ms(1500);\n }\n \/\/EPSILON *= 0.99;\n }\n }\n}请你总结一下上面的代码"},{"role":"assistant","content":{"answer":"这段代码实现了一个基于Q学习（Q-learning）的强化学习算法，用于训练一个小车（或类似设备）在不同状态下采取动作，从而获得奖励并不断改进决策策略。主要包含以下几个部分：\n\n1. select_action函数 \n • 目的：根据当前状态选择下一步采取的动作。 \n • 机制：采用ε-贪心策略（ε-greedy）。 \n  – 如果以一定概率（由EPSILON决定，例如10%）选择随机动作，则返回一个随机动作； \n  – 否则遍历当前状态下所有可能动作的Q值，选择Q值最大的动作（此处采用“>=”比较，若多个动作Q值相同，则会返回后出现的动作）。\n\n2. take_action_and_get_reward函数 \n • 目的：根据传入的动作让小车执行对应的动作，并反馈执行后的奖励。 \n • 实现： \n  – 根据动作（0、1、2分别对应左转、右转、向前行驶）调用相应的函数（如Car_SpinLeft、Car_SpinRight、Car_Run），然后延时一定时间以等待动作执行。 \n  – 执行动作后得到新的状态（调用get_state函数），根据状态判断奖励： \n   ○ 当新状态为b1001时，奖励较高（3分）； \n   ○ 当状态为b1011或b1101时，奖励为0； \n   ○ 其他状态则给负奖励（-1分）。\n\n3. update_q_value函数 \n • 目的：依据Q学习的更新公式更新Q表中的值。 \n • 原理： \n  – 寻找新状态下所有动作中最大的Q值，即max_q； \n  – 使用更新公式： \n   Q(state, action) += ALPHA * [reward + GAMMA * max_q - Q(state, action)] \n  其中ALPHA为学习率，GAMMA为折扣因子。\n\n4. train函数 \n • 目的：进行多次训练（例如1000个episode）以不断优化Q表。 \n • 过程： \n  – 每个episode开始时获取当前状态； \n  – 在无限循环内： \n   ○ 选择动作（调用select_action）； \n   ○ 执行动作并获得奖励（调用take_action_and_get_reward）； \n   ○ 获取新状态并更新Q值（调用update_q_value）； \n   ○ 将状态更新为新状态； \n  – 如果检测到特殊状态（如b1111），则调用Car_Stop停止小车，并等待小车恢复到指定状态（等待get_state返回b1001）后延时一小段时间。 \n  – （注释掉的EPSILON衰减语句提示可以逐渐减少随机探索的概率。）\n\n总结： \n这段代码通过不断地在环境中执行动作、获取反馈奖励和更新Q表，来训练一个基于Q学习的智能行为控制器，从而使小车能够在各种状态下选择最优动作。整个过程包括探索（随机选动作）和利用（选择当前最优动作）的权衡，并利用Q学习的核心更新公式来逐步优化决策策略。"}}],"tstamp":1744155064.433044672,"metadata":{"token_len":722,"header_count":{"h1":0,"h2":0,"h3":0,"h4":0,"h5":0,"h6":0},"list_count":{"ordered":4,"unordered":0},"bold_count":{"**":0,"__":0}}} +{"uid":"06add81598044afd","ans_id":"SkaYCnNBkC5ZkR67fJ6xD8","model":"o3-mini-2025-01-31","messages":[{"role":"user","content":"write me a python script to download music from yt without api"},{"role":"assistant","content":{"answer":"Below is a sample Python script that uses the third‐party library “pytube” to download the audio from a YouTube video (i.e. extract “music”). This script does not use the official YouTube API. (Important: Please make sure you comply with YouTube’s Terms of Service and copyright laws when downloading any content.)\n\nSetup:\n1. Install pytube by running:\n  pip install pytube\n\n2. (Optional) If you want to convert the downloaded file (typically an MP4 containing audio) into MP3, you’ll need a tool like ffmpeg installed on your system.\n\nBelow is the Python script:\n\n----------------------------------------------------\n#!\/usr\/bin\/env python3\nimport os\nfrom pytube import YouTube\n\ndef download_audio(url, output_folder=\".\"):\n try:\n # Create YouTube object for the given URL\n yt = YouTube(url)\n except Exception as e:\n print(\"Error creating YouTube object:\", e)\n return\n\n # Filter out streams that contain only audio\n audio_stream = yt.streams.filter(only_audio=True).first()\n if audio_stream is None:\n print(\"No audio stream available for this video!\")\n return\n\n try:\n print(f\"Downloading: {yt.title}\")\n # Download the audio stream.\n downloaded_file = audio_stream.download(output_path=output_folder)\n print(\"Download complete!\")\n \n # Optionally: rename the file extension to '.mp3'\n # Note: This does not convert the file to mp3. For a proper conversion, use a library such as pydub or subprocess with ffmpeg.\n base, ext = os.path.splitext(downloaded_file)\n new_file = base + \".mp3\"\n os.rename(downloaded_file, new_file)\n print(f\"File saved as: {new_file}\")\n except Exception as e:\n print(\"Error during download:\", e)\n\ndef main():\n # Ask the user for the YouTube URL and output folder if desired.\n url = input(\"Enter the YouTube video URL: \").strip()\n if not url:\n print(\"No URL provided!\")\n return\n\n output_folder = input(\"Enter the output folder (press Enter for current directory): \").strip()\n if not output_folder:\n output_folder = \".\"\n elif not os.path.exists(output_folder):\n print(\"Output folder does not exist. Creating it.\")\n os.makedirs(output_folder)\n\n download_audio(url, output_folder)\n\nif __name__ == '__main__':\n main()\n\n----------------------------------------------------\n\nNotes:\n• This script creates a YouTube object from the provided URL using pytube.\n• It then filters the streams to select an audio-only stream and downloads it.\n• After downloading, the file is renamed to have a .mp3 extension. (Keep in mind that a simple file rename does not perform an actual audio conversion. For converting formats properly, you could use ffmpeg via a Python wrapper like pydub.)\n• Always ensure your usage complies with YouTube’s Terms of Service.\n\nHappy coding!"}}],"tstamp":1744155088.5161111355,"metadata":{"token_len":623,"header_count":{"h1":0,"h2":0,"h3":0,"h4":0,"h5":0,"h6":0},"list_count":{"ordered":2,"unordered":0},"bold_count":{"**":0,"__":2}}} +{"uid":"0775b8a6be1a4d07","ans_id":"AG6SoiKeMrFDVUrqZBURf8","model":"o3-mini-2025-01-31","messages":[{"role":"user","content":"Ebac Prompt\n\n\nYou're a genius catalog SEO Manager making product descriptions. Here are your instructions:\n\n\n1st Header: A meta and SEO certified sellable header to introduce the product in a way that is emotional or scenario driven, with the rearrange form of the product name, making sure it won't be the same as the next headers. (Ex: an uplifiting scenario : product name \/ With product name have this benefit)\n\n1st Body: An emotional driven 3 short paragraph description of the product with meta SEO words. Add Scenarios but don't make it repetative. \n\n2nd Header: A meta and SEO certified problem question and with the rearrange form of the product name, making sure it won't be the same as the next headers. (Ex: problem question? product is here.)\n\n2nd Body: a short spiel on how the the product solves the problem. Then list the features\/benefits of the product in such way that it is listed this way Feature\/benefit: details. \n\n3rd Header: A meta and SEO certified way of saying this product is and advantage to have this is the future and this is how it works. (Example: Gain somethin with product name: The future of )\n\n3rd Body: a short spiel on how the buyer is of advantage using this product, and using the product in different settings would be their advantage. Then this is how it works. List the steps as it is in the data. Then End it with a scenario in the future with this product with a buy now spiel. \n\n4th Body:\n\nGive me a list of what's in the box\nusing this format\nitem (model number) - quanitity unit\n\n5th Header: a Meta SEO certified header about the specifications of the product\n\n6th Header: a Meta SEO certified header about the manuals of the product\n\n\nHere's your data:\n\n\nName: \n\nEBac CD200 Industrial Dehumidifier - 138 PPD | 664 CFM\n\n\nBody:\n\nEBAC Desiccant Dehumidifier - Precision Beyond Ordinary\nUnleash superior dehumidification with the EBAC Desiccant Dehumidifiers, a pinnacle of precision and efficiency. Crafted with stainless steel strength, this powerhouse boasts auto\/manual mode selection, electronic controls, and a high-capacity PTC Heater for immediate and sustained drying.\n\nDD200 and DD300 Key Features:\nStainless Steel Construction\nAuto \/ Manual Mode Selection\nElectronic Controls\nHigh Capacity PTC Heater\nRemote Humidistat Facility\nLow Temperature Operation\nAmmeter\nWhy Choose EIPL: As Europe's leading dehumidifier manufacturer, EIPL ensures reliability in the harshest conditions. With over 37 years of expertise, our dehumidifiers stand the test of time, trusted by the plant hire trade for their ruggedness and outstanding performance.\n\nPrecision in Design: The DD200 & DD300's compact, rugged design, coupled with a PTC Heater, guarantees swift and consistent drying. Monitor efficiency with the built-in ammeter and hours run meter. The unit offers manual\/auto control and accommodates a remote humidistat for seamless operation.\n\nHigh-Efficiency Patented PPS Rotor: The heart of our dehumidifiers lies in the patented PPS Rotor, with 82% active Silica Gel, ensuring peak performance across diverse environments. Washable and designed for longevity, it epitomizes our commitment to excellence.\n\nIgnite Comfort - Opt for EBAC Desiccant Dehumidifiers Now!\n\nHow a dehumidifier works diagram\nHow Our Dehumidifier Works:\nProcess air is drawn into the dehumidifier\nProcess air passes over a wheel impregnated with silica gel.\nThe silica gel absorbs the moisture from the air.\nProcess air leaves the dehumidifier as warm dry air.\nThe silica gel wheel continually rotates.\nRegeneration air is heated to a high temperature and passed over a segment of the wheel\nSilica gel releases the moisture from the wheel into the regeneration air.\nRegeneration air leaves the dehumidifier as warm wet air and exhausted outside.\nManuals of DD200 and DD300 Dehumidifiers\nProduct Sheet of DD200\nProduct Sheet of DD300\nOperation Manual of DD200\nOperation Manual of DD300\nWiring Diagram of DD200\nWiring Diagram of DD300\nWiring Schematic of DD200\nWiring Schematic of DD300\nSpare Parts List of DD200\nSpare Parts List of DD300\nWarranty\nSpecs of Desiccant Dehumidifiers\nSpecification\tDD200\tDD300\nHeight (inch)\t13.3\t14.5\nWidth (inch)\t13.0\t14.2\nDepth (inch)\t15.0\t17.0\nWeight (lbs)\t37.5\t44\nVoltage\t110\t110\nPhase\t1\t1\nFrequency (Hz)\t60\t60\nPower (kW)\t0.8\t1.4\nCurrent (A)\t7.5\t12.4\nProcess Airflow (cfm)\t115\t136\nRegen Airflow (cfm)\t38\t42\nProcess Duct Size (inch)\t5.0\t5.0\nRegen Duct Size (inch)\t2.75\t2.75\nNoise Level (dba)\t67\t67\nTypical Extraction (ppd)\t36\t69\nMin Operating Temp (°F)\t-4\t-4\nMax Operating Temp (°F)\t104\t104\nAdditional Features of the Desiccant Dehumidifiers\nFeature\tDD200\tDD300\nOn\/Off Switch\tY\tY\nAmmeter\tY\tY\nElectronic Controls\tY\tY\nManual\/Auto Modes\tY\tY\nRemote Humidistat Facility\tY\tY\nHours Run Meter\tY\tY\nFitted Mains Plug\tY\tY\nFan Speeds\t1\t1\nHigh Capacity PTC Heater\tY\tY\nInlet Air Filters\tY\tY\nRubber Anti Vibration Feet\tY\tY\nSingle Air Inlet Design\tY\tY\nFree Standing\tY\tY\nStainless Steel Construction\tR407c\tR407c\nHigh Temperature Cut Outs\tY\tY\nEBAC Desiccant Dehumidifier DD200 DD\n\n\n\n\n\n\n\nKeywords to use:\n\nIndustrial dehumidifiers\nCommercial dehumidifiers\nHeavy-duty dehumidifiers\nIndustrial-grade dehumidifiers\nLarge-scale dehumidifiers\nHigh-capacity dehumidifiers\nDehumidifiers for industrial use\nCommercial-grade dehumidifiers\nIndustrial moisture control\nIndustrial humidity control\nDehumidifiers for warehouses\nFactory dehumidifiers\nIndustrial air dryers\nCommercial moisture removal\nIndustrial drying solutions\nIndustrial climate control\nIndustrial moisture management\nIndustrial air quality solutions\nWarehouse humidity control\nManufacturing dehumidifiers\nIndustrial dehumidification systems\nDehumidifiers for industrial applications\nHeavy-duty moisture control\nIndustrial-grade moisture removal\nCommercial air dryers\nIndustrial dehumidifier rental\nLarge commercial dehumidifiers\nIndustrial moisture solutions\nIndustrial drying equipment\nFactory humidity control\nCommercial air quality\nIndustrial environment control\nDehumidifiers for factories\nIndustrial air dehumidifiers\nCommercial drying solutions\nIndustrial drying technology\nHumidity control for industries\nIndustrial air management\nIndustrial dehumidifier systems\nWarehouse dehumidifiers\nIndustrial HVAC systems\nHeavy-duty dehumidification\nIndustrial moisture protection\nIndustrial air conditioning\nCommercial environment control\nDehumidifiers for commercial spaces\nIndustrial air purification\nCommercial humidity solutions\nLarge-scale moisture control\nHeavy-duty humidity control\nIndustrial drying devices\nIndustrial moisture removal equipment\nDehumidifiers for large spaces\nIndustrial climate management\nCommercial humidity management\nIndustrial moisture control systems\nWarehouse drying solutions\nIndustrial air treatment\nDehumidifiers for production facilities\nHeavy-duty air dryers\nIndustrial air conditioning systems\nCommercial drying technology\nIndustrial air quality control\nDehumidifiers for storage facilities\nCommercial dehumidification\nIndustrial moisture control solutions\nWarehouse air dryers\nIndustrial air systems\nFactory dehumidification\nLarge industrial dehumidifiers\nHeavy-duty air management\nIndustrial dehumidifier units\nCommercial moisture control systems\nIndustrial drying management\nHumidity control for warehouses\nDehumidifiers for industrial environments\nIndustrial air quality management\nHeavy-duty drying solutions\nCommercial air management\nIndustrial moisture mitigation\nWarehouse humidity solutions\nIndustrial drying control\nDehumidifiers for large warehouses\nIndustrial air dehumidification\nCommercial air drying\nIndustrial moisture regulation\nHeavy-duty environment control\nIndustrial moisture prevention\nDehumidifiers for large facilities\nCommercial climate control\nIndustrial air quality improvement\nIndustrial drying units\nHeavy-duty air quality\nIndustrial moisture reduction\nWarehouse air quality solutions\nDehumidifiers for large spaces\nIndustrial air humidity control\nCommercial drying management\nIndustrial environment solutions\nHeavy-duty climate management"},{"role":"assistant","content":{"answer":"1st Header: Transform Your Industrial Climate with EBac CD200 – Experience Precision and Comfort Now!\n\n1st Body: \nStep into a world where industrial humidity control is redefined by the power of the EBac CD200 Industrial Dehumidifier. Imagine a busy manufacturing floor or a sprawling warehouse that suddenly feels lighter, drier, and more comfortable—all thanks to advanced dehumidification technology designed for industrial environments. With breakthrough performance in heavy-duty dehumidifiers, every moment is a step toward harnessing pure, dry air.\n\nEmbrace a safer, more efficient workspace where every industrial-grade dehumidifier offers not just moisture control but also a promise of reliability. Whether you’re managing production lines or coordinating large-scale industrial air treatment, the EBac CD200 stands as a stalwart solution for commercial air quality and industrial drying equipment. Enjoy the difference as your facility transforms into an environment optimized for both worker comfort and operational efficiency.\n\nFeel the energy as your industrial moisture management challenges melt away. With a design inspired by over 37 years of industry expertise, this high-capacity dehumidifier is engineered for durability, precision, and relentless performance. Trust in a solution that leads the way in industrial dehumidification systems and commercial moisture removal.\n\n2nd Header: Facing Persistent Industrial Humidity? Discover the EBac CD200 Solution!\n\n2nd Body: \nThe EBac CD200 Industrial Dehumidifier is engineered to conquer moisture challenges head-on, ensuring a drier, safer industrial space. This heavy-duty dehumidifier for industrial use comes loaded with features designed to deliver optimum performance even in the harshest conditions. \n\n• Stainless Steel Construction: Robust build that withstands tough industrial environments. \n• Auto\/Manual Mode Selection: Effortless switching for tailored moisture control. \n• Electronic Controls: Intuitive operation for precise and reliable performance. \n• High Capacity PTC Heater: Rapid moisture extraction for immediate drying. \n• Remote Humidistat Facility: Convenient monitoring and adjustment even from a distance. \n• Low Temperature Operation: Effective functionality in cold settings. \n• Ammeter: Built-in monitoring to track electrical performance.\n\n3rd Header: Gain the Future-Ready Advantage with EBac CD200 – The Next Evolution in Industrial Air Drying!\n\n3rd Body: \nStep into tomorrow with the EBac CD200, a dehumidifier engineered to boost industrial air quality and transform your workplace environment. Whether it’s for manufacturing floors, warehouses, or production facilities, this commercial dehumidifier offers a clear advantage through advanced design and innovation. Embrace different settings—from busy factory floors to storage facilities—and enjoy unparalleled industrial moisture regulation. \n\nThis is how it works: \n1. Process air is drawn into the dehumidifier. \n2. Process air passes over a wheel impregnated with silica gel. \n3. The silica gel absorbs the moisture from the air. \n4. Process air leaves the dehumidifier as warm dry air. \n5. The silica gel wheel continually rotates. \n6. Regeneration air is heated to a high temperature and passed over a segment of the wheel. \n7. Silica gel releases the moisture from the wheel into the regeneration air. \n8. Regeneration air leaves the dehumidifier as warm wet air and is exhausted outside.\n\nImagine a future where industrial moisture challenges no longer hinder productivity—a future where operational efficiency and worker comfort go hand in hand. Don’t wait, secure your EBac CD200 today and lead your facility into a new era of industrial climate control!\n\n4th Body: \nEBac CD200 Industrial Dehumidifier (CD200) - 1 Unit \nPower Cable (PTC-664) - 1 Unit \nOperation Manual (DD200\/DD300) - 1 Packet \nInstallation Guide (CD200-IG) - 1 Packet \n\n5th Header: EBac CD200 Product Specs – Unrivaled Industrial Dehumidifier Specifications for Superior Performance\n\n6th Header: EBac CD200 Manuals – Complete Guides and Schematics for Seamless Installation and Operation"}}],"tstamp":1744155065.5584082603,"metadata":{"token_len":813,"header_count":{"h1":0,"h2":0,"h3":0,"h4":0,"h5":0,"h6":0},"list_count":{"ordered":8,"unordered":0},"bold_count":{"**":0,"__":0}}} +{"uid":"07d8ea70be2b4a17","ans_id":"d62vyVsxem3xZe8fUJofs2","model":"o3-mini-2025-01-31","messages":[{"role":"user","content":"How is it possible to add `\\printbibliography` to the table of contents (`\\tableofcontents`) when using `\\usepackage[backend=bibtex,urldate=iso]{biblatex}`?"},{"role":"assistant","content":{"answer":"When you load biblatex with\n\n  \\usepackage[backend=bibtex,urldate=iso]{biblatex}\n\nyou can have the bibliography automatically added to the table of contents simply by using the built‐in heading called bibintoc. In other words, instead of writing\n\n  \\printbibliography\n\nyou write\n\n  \\printbibliography[heading=bibintoc]\n\nThis option tells biblatex to use its predefined heading that invokes (internally) an \\addcontentsline command. That way, an entry for the bibliography section is automatically added to your table of contents.\n\nAlternatively, if you want to customize the bibliography heading further, you can define your own heading. For example:\n\n  \\defbibheading{bibintoc}[Bibliography]{%\n   \\section*{Bibliography}%\n   \\addcontentsline{toc}{section}{Bibliography}%\n  }\n\nThen, using\n\n  \\printbibliography[heading=bibintoc]\n\nwill create the bibliography with the custom heading and add it to the table of contents.\n\nEither method achieves the goal of having the bibliography appear in the table of contents when using biblatex."}}],"tstamp":1744155064.2242491245,"metadata":{"token_len":245,"header_count":{"h1":0,"h2":0,"h3":0,"h4":0,"h5":0,"h6":0},"list_count":{"ordered":0,"unordered":0},"bold_count":{"**":0,"__":0}}} +{"uid":"083cbf8d1ec54d91","ans_id":"TJLPUqtLYRuNCW7FHQUEus","model":"o3-mini-2025-01-31","messages":[{"role":"user","content":"System\tYour task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code’s performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.\nUser\tdef fibonacci(n):\nif n <= 0:\nreturn []\nelif n == 1:\nreturn [0]\nelif n == 2:\nreturn [0, 1]\nelse:\nfib = [0, 1]\nfor i in range(2, n):\nfib.append(fib[i-1] + fib[i-2])\nreturn fib"},{"role":"assistant","content":{"answer":"Below is the original code for reference:\n\n--------------------------------------------------\ndef fibonacci(n):\n if n <= 0:\n return []\n elif n == 1:\n return [0]\n elif n == 2:\n return [0, 1]\n else:\n fib = [0, 1]\n for i in range(2, n):\n fib.append(fib[i-1] + fib[i-2])\n return fib\n--------------------------------------------------\n\nThe algorithm is already O(n) in time and uses O(n) space, which is asymptotically optimal for this problem. However, there are several micro-level improvements that can make the code a little faster or more efficient in practice. Here are some suggestions:\n\n1. Simplify the Edge Cases and Loop\n • Instead of handling n == 1 and n == 2 separately, you can initialize the list correctly and then run the loop starting from index 2 if n > 1.\n • Use negative indexing (fib[-1], fib[-2]), which is O(1) and simplifies the code.\n \n Example:\n --------------------------------------------------\n def fibonacci(n):\n if n <= 0:\n return []\n fib = [0]\n if n > 1:\n fib.append(1)\n for _ in range(2, n):\n fib.append(fib[-1] + fib[-2])\n return fib\n --------------------------------------------------\n \n Explanation: This version first creates the list with the correct base values. There is no need for three separate if\/elif checks—this makes the code a little cleaner and easier to maintain.\n\n2. Preallocate the List (Low-Level Micro-Optimization)\n • Python’s list append is amortized O(1), but when n is very large, preallocating a list and then filling it by index can reduce the number of memory reallocations.\n \n Example:\n --------------------------------------------------\n def fibonacci(n):\n if n <= 0:\n return []\n fib = [0] * n # Preallocate list of size n\n fib[0] = 0\n if n > 1:\n fib[1] = 1\n for i in range(2, n):\n fib[i] = fib[i-1] + fib[i-2]\n return fib\n --------------------------------------------------\n \n Explanation: Preallocating the list avoids the overhead of dynamic resizing that can occur with repeated calls to append. This can be especially beneficial when generating very large Fibonacci sequences.\n\n3. Use Local Variables for Loop Calculation\n • Sometimes in performance-critical loops, reducing the repeated attribute lookups or list indexing can help. You can update two temporary variables (say, a and b) and then append b to the result list.\n \n Example:\n --------------------------------------------------\n def fibonacci(n):\n if n <= 0:\n return []\n if n == 1:\n return [0]\n a, b = 0, 1\n result = [a, b]\n for _ in range(2, n):\n a, b = b, a + b\n result.append(b)\n return result\n --------------------------------------------------\n \n Explanation: By keeping track of the last two Fibonacci numbers in local variables, you avoid the repeated indexing into the list. This change might seem minor but can have measurable benefits in tight loops when n is huge.\n\n4. Consider a Generator if Full List Storage Is Not Needed\n • If you are just iterating over Fibonacci numbers (and don’t need random access), you could also return a generator. While this changes how the user interacts with the result, it is much more memory efficient for very large n.\n \n Example:\n --------------------------------------------------\n def fibonacci_gen(n):\n a, b = 0, 1\n for _ in range(n):\n yield a\n a, b = b, a + b\n --------------------------------------------------\n \n Explanation: This generator yields one Fibonacci number at a time and does not store the entire sequence in memory. (However, note that this changes the functionality: the output is an iterator rather than a list, so use it only if laziness is acceptable.)\n\nIn summary, while the original code is functionally correct and asymptotically optimal, the above modifications can improve performance in practice—especially when dealing with large inputs—by reducing memory reallocation overhead, decreasing repeated indexing, and cleaning up the control flow. Choose the version of the code that best meets your performance and usability requirements while maintaining the same overall functionality."}}],"tstamp":1744155073.0418286324,"metadata":{"token_len":935,"header_count":{"h1":0,"h2":0,"h3":0,"h4":0,"h5":0,"h6":0},"list_count":{"ordered":4,"unordered":0},"bold_count":{"**":0,"__":0}}} +{"uid":"0919c1c5c5c34f7d","ans_id":"jPeZSaRYkjn3BcHGajERUV","model":"o3-mini-2025-01-31","messages":[{"role":"user","content":"You are an expert iOS developer. For my SwiftUI project I need to record the voice and save it. I also like to have equalizer like effect in the middle of the view to show to the user that it is recording. In the view there are two buttons. One for start the recording, and the other for stopping and finishing the recording. Now use best practices and software design pattern to generate the all necessary code. "},{"role":"assistant","content":{"answer":"Below is one complete solution that uses the MVVM pattern, separates audio‐recording logic from the SwiftUI view, and implements an audio “equalizer” effect by sampling the audio level from the recorder. You can, of course, refine the equalizer view as desired. In this example the view model uses AVFoundation to request permission, configure and start recording, and then set up a timer to periodically update a published “power” value for the UI. The UI then maps that power into a set of animated vertical bars. (Note that error handling and extra permission‐handling might be expanded for your production app.)\n\n–––––––––––––––––––––––––––––––––––––––––––––––––––––\nFile: AudioRecorderViewModel.swift\n\nimport AVFoundation\nimport SwiftUI\n\nclass AudioRecorderViewModel: ObservableObject {\n \/\/ Published properties for the view.\n @Published var isRecording: Bool = false\n @Published var audioPower: Float = 0.0 \/\/ dB level from AVAudioRecorder\n \n private var audioRecorder: AVAudioRecorder?\n private var meterTimer: Timer?\n \n \/\/ Create a unique file URL in temporary directory for our recording.\n private func recordingFileURL() -> URL {\n let fileName = UUID().uuidString + \".m4a\"\n return FileManager.default.temporaryDirectory.appendingPathComponent(fileName)\n }\n \n \/\/ Starts recording the audio.\n func startRecording() {\n \/\/ Request microphone permission first.\n AVAudioSession.sharedInstance().requestRecordPermission { [weak self] allowed in\n guard let self = self else { return }\n DispatchQueue.main.async {\n guard allowed else {\n \/\/ Handle permission denial appropriately.\n print(\"Microphone permission not granted\")\n return\n }\n \n do {\n \/\/ Setup the audio session.\n let audioSession = AVAudioSession.sharedInstance()\n try audioSession.setCategory(.playAndRecord, mode: .default, options: [])\n try audioSession.setActive(true)\n \n \/\/ Define recording settings.\n let settings: [String: Any] = [\n AVFormatIDKey: Int(kAudioFormatMPEG4AAC),\n AVSampleRateKey: 12000,\n AVNumberOfChannelsKey: 1,\n AVEncoderAudioQualityKey: AVAudioQuality.high.rawValue\n ]\n \n let url = self.recordingFileURL()\n self.audioRecorder = try AVAudioRecorder(url: url, settings: settings)\n self.audioRecorder?.isMeteringEnabled = true\n self.audioRecorder?.prepareToRecord()\n self.audioRecorder?.record()\n self.isRecording = true\n \n \/\/ Start periodic updates of the recorder’s average power.\n self.meterTimer = Timer.scheduledTimer(withTimeInterval: 0.1, repeats: true) { _ in\n self.audioRecorder?.updateMeters()\n if let power = self.audioRecorder?.averagePower(forChannel: 0) {\n self.audioPower = power\n }\n }\n } catch {\n print(\"Failed to set up recorder: \\(error.localizedDescription)\")\n }\n }\n }\n }\n \n \/\/ Stops the recording.\n func stopRecording() {\n audioRecorder?.stop()\n meterTimer?.invalidate()\n meterTimer = nil\n isRecording = false\n }\n \n deinit {\n \/\/ Clean up the timer if the view model is deallocated.\n meterTimer?.invalidate()\n }\n}\n\n–––––––––––––––––––––––––––––––––––––––––––––––––––––\nFile: EqualizerView.swift\n\nimport SwiftUI\n\n\/\/\/ A simple view that displays several animated bars whose heights reflect the current audio power level.\n\/\/\/ You might adjust the algorithm to suit your design.\nstruct EqualizerView: View {\n \/\/ dB values are typically between -160 and 0. Here we “normalize” them to 0..1.\n \/\/ Adjust these calculations to scale the view as desired.\n private func normalizedPower(from db: Float) -> CGFloat {\n \/\/ For our simple scale, we consider values from -60 dB (silence) to 0 dB (loud)\n let level = max(db, -60) \/\/ clamp to -60 at minimum\n return CGFloat((level + 60) \/ 60)\n }\n \n let audioPower: Float\n let isRecording: Bool\n \n \/\/ The number of bars in the equalizer.\n private let numberOfBars = 5\n \n var body: some View {\n HStack(alignment: .bottom, spacing: 4) {\n ForEach(0.. np.array:\n \"\"\"\n Main transform method.\n :param X: Input predictions to neutralize. \n :param features: DataFrame with features for neutralization. \n :param era_series: Series with era labels for each row in features. \n Features, eras and the prediction column must all have the same length.\n :return: Penalized predictions.\n \"\"\"\n assert len(X) == len(features), \"Input predictions must have same length as features.\"\n assert len(X) == len(era_series), \"Input predictions must have same length as eras.\"\n df = features.copy()\n df[\"prediction\"] = X\n df[\"era\"] = era_series\n penalized_data = self._reduce_all_exposures(\n dataf=df, column=self.pred_name, neutralizers=list(features.columns)\n )\n return penalized_data\n\n def _reduce_all_exposures(\n self,\n dataf: pd.DataFrame,\n column: str = \"prediction\",\n neutralizers: list = None,\n normalize=True,\n gaussianize=True,\n ) -> pd.DataFrame:\n neutralized = []\n\n for era in tqdm(dataf[\"era\"].unique()):\n dataf_era = dataf[dataf[\"era\"] == era]\n scores = dataf_era[[column]].values\n exposure_values = dataf_era[neutralizers].values\n\n if normalize:\n scores2 = []\n for x in scores.T:\n x = (scipy.stats.rankdata(x, method=\"ordinal\") - 0.5) \/ len(x)\n if gaussianize:\n x = scipy.stats.norm.ppf(x)\n scores2.append(x)\n scores = np.array(scores2)[0]\n\n scores, _ = self._reduce_exposure(\n scores, exposure_values, len(neutralizers), None\n )\n\n scores \/= tf.math.reduce_std(scores)\n scores -= tf.reduce_min(scores)\n scores \/= tf.reduce_max(scores)\n neutralized.append(scores.numpy())\n\n predictions = pd.DataFrame(\n np.concatenate(neutralized), columns=[column], index=dataf.index\n )\n return predictions\n\n def _reduce_exposure(self, prediction, features, input_size=50, weights=None):\n model = tf.keras.models.Sequential(\n [\n tf.keras.layers.Input(input_size),\n tf.keras.experimental.LinearModel(use_bias=False),\n ]\n )\n feats = tf.convert_to_tensor(features - 0.5, dtype=tf.float32)\n pred = tf.convert_to_tensor(prediction, dtype=tf.float32)\n if weights is None:\n optimizer = tf.keras.optimizers.Adamax()\n start_exp = self.__exposures(feats, pred[:, None])\n target_exps = tf.clip_by_value(\n start_exp, -self.max_exposure, self.max_exposure\n )\n self._train_loop(model, optimizer, feats, pred, target_exps)\n else:\n model.set_weights(weights)\n return pred[:, None] - model(feats), model.get_weights()\n\n def _train_loop(self, model, optimizer, feats, pred, target_exps):\n for _ in range(1000000):\n loss, grads = self.__train_loop_body(model, feats, pred, target_exps)\n optimizer.apply_gradients(zip(grads, model.trainable_variables))\n if loss < 1e-7:\n break\n\n def __train_loop_body(self, model, feats, pred, target_exps):\n with tf.GradientTape() as tape:\n exps = self.__exposures(feats, pred[:, None] - model(feats, training=True))\n loss = tf.reduce_sum(\n tf.nn.relu(tf.nn.relu(exps) - tf.nn.relu(target_exps))\n + tf.nn.relu(tf.nn.relu(-exps) - tf.nn.relu(-target_exps))\n )\n return loss, tape.gradient(loss, model.trainable_variables)\n\n @staticmethod\n def __exposures(x, y):\n x = x - tf.math.reduce_mean(x, axis=0)\n x = x \/ tf.norm(x, axis=0)\n y = y - tf.math.reduce_mean(y, axis=0)\n y = y \/ tf.norm(y, axis=0)\n return tf.matmul(x, y, transpose_a=True)\n---\n\nimport os\nimport pathlib\nimport numpy as np\nimport pandas as pd\nimport scipy.stats\nimport tensorflow as tf\nimport joblib\nfrom tqdm.notebook import tqdm\n\nNUMERAI_S3_BUCKET_URL = \"https:\/\/numerai-public-datasets.s3-us-west-2.amazonaws.com\"\n\n#read in the example predictions from local storage\n#EXAMPLE_PREDS = 'tournament_predictions.csv'\n\n#or downlod the example predictions from Numerai's S3 bucket:\nEXAMPLE_PREDS_URL = NUMERAI_S3_BUCKET_URL + \"\/latest_numerai_example_predictions_data.csv.xz\"\n\n#download the latest tournament data file:\nTOURNAMENT_DATA_URL = NUMERAI_S3_BUCKET_URL + \"\/latest_numerai_tournament_data.csv.xz\"\n\n###IMPORTANT! DELETE THE FILE BELOW IF YOU CHANGE MODELS! OTHERWISE, RENAME THE FILE FOR YOUR VARIOUS MODELS###\nLM_CACHE_FILE = pathlib.Path(\"neutralization.cache.joblib\")\n\n@tf.function(experimental_relax_shapes=True, experimental_compile=True)\ndef exposures(x, y):\n x = x - tf.math.reduce_mean(x, axis=0)\n x = x \/ tf.norm(x, axis=0)\n y = y - tf.math.reduce_mean(y, axis=0)\n y = y \/ tf.norm(y, axis=0)\n return tf.matmul(x, y, transpose_a=True)\n\n@tf.function(experimental_relax_shapes=True)\ndef train_loop_body(model, feats, pred, target_exps):\n with tf.GradientTape() as tape:\n exps = exposures(feats, pred[:, None] - model(feats, training=True))\n loss = tf.reduce_sum(tf.nn.relu(tf.nn.relu(exps) - tf.nn.relu(target_exps)) +\n tf.nn.relu(tf.nn.relu(-exps) - tf.nn.relu(-target_exps)))\n return loss, tape.gradient(loss, model.trainable_variables)\n\ndef train_loop(model, optimizer, feats, pred, target_exps, era):\n for i in range(1000000):\n loss, grads = train_loop_body(model, feats, pred, target_exps)\n optimizer.apply_gradients(zip(grads, model.trainable_variables))\n if loss < 1e-7:\n break\n if i % 10000 == 0:\n tqdm.write(f'era: {era[3:]} loss: {loss:0.7f}', end='\\r')\n \ndef reduce_exposure(prediction, features, max_exp, era, weights=None):\n model = tf.keras.models.Sequential([\n tf.keras.layers.Input(310),\n tf.keras.experimental.LinearModel(use_bias=False),\n ])\n feats = tf.convert_to_tensor(features - 0.5, dtype=tf.float32)\n pred = tf.convert_to_tensor(prediction, dtype=tf.float32)\n if weights is None:\n optimizer = tf.keras.optimizers.Adamax()\n start_exp = exposures(feats, pred[:, None])\n target_exps = tf.clip_by_value(start_exp, -max_exp, max_exp)\n train_loop(model, optimizer, feats, pred, target_exps, era)\n else:\n model.set_weights(weights)\n return pred[:,None] - model(feats), model.get_weights()\n\ndef reduce_all_exposures(df, column=[\"prediction\"], neutralizers=None,\n normalize=True,\n gaussianize=True,\n era_col=\"era\",\n max_exp=0.1): ###<-----SELECT YOUR MAXIMUM FEATURE EXPOSURE HERE###\n if neutralizers is None:\n neutralizers = [x for x in df.columns if x.startswith(\"feature\")]\n neutralized = []\n if LM_CACHE_FILE.is_file():\n cache = joblib.load(LM_CACHE_FILE)\n # Remove weights for eraX if we'd accidentally saved it in the past.\n cache.pop(\"eraX\", None)\n else:\n cache = {}\n for era in tqdm(df[era_col].unique()):\n tqdm.write(era, end='\\r')\n df_era = df[df[era_col] == era]\n scores = df_era[column].values\n exposure_values = df_era[neutralizers].values\n\n if normalize:\n scores2 = []\n for x in scores.T:\n x = (scipy.stats.rankdata(x, method='ordinal') - .5) \/ len(x)\n if gaussianize:\n x = scipy.stats.norm.ppf(x)\n scores2.append(x)\n scores = np.array(scores2)[0]\n\n scores, weights = reduce_exposure(scores, exposure_values,\n max_exp, era, cache.get(era))\n if era not in cache and era != \"eraX\":\n cache[era] = weights\n joblib.dump(cache, LM_CACHE_FILE)\n scores \/= tf.math.reduce_std(scores)\n scores -= tf.reduce_min(scores)\n scores \/= tf.reduce_max(scores)\n neutralized.append(scores.numpy())\n\n predictions = pd.DataFrame(np.concatenate(neutralized),\n columns=column, index=df.index)\n return predictions\n\n#If CUDA isn't set up properly for Tensorflow, then at least maximize the number of threads available for CPU\nif not tf.config.list_physical_devices('GPU'): # No GPU(s) found\n tf.config.threading.set_inter_op_parallelism_threads(2)\n tf.config.threading.set_intra_op_parallelism_threads(os.cpu_count() \/\/ 2)\n\n#read-in or download the example predictions\n\nexp_df = pd.read_csv(EXAMPLE_PREDS_URL, index_col=0)\n\n#download the tournament data\ntournament_df = pd.read_csv(TOURNAMENT_DATA_URL, index_col=0)\n\n#merge them together\nfull_df = pd.merge(tournament_df, exp_df, left_index=True, right_index=True)\n#this cell executes the full script above and neutralizes the predictions to achieve a maximum 0.1 Feature Exposure\nneutralized_df = reduce_all_exposures(full_df)\n----\n\nthese two pieces of code accomplish the same thing. do you see another way to do it with jax? first explain what the process is that is going on. like a linear model is being found that, when subtracted, leaves the predictions with a \"feature exposure(?)\" that is less than a pre-specified amount. comment the code to also explain what's going on where"},{"role":"assistant","content":{"answer":"Below is one way to do it with JAX. First, let’s explain what the original code is doing:\n\n──────────────────────────────\nExplanation\n\nYou start with raw predictions and a set of feature values (the “neutralizers”). Because many of these features are “exposed” (i.e. correlated) with the predictions, you want to “neutralize” or reduce that unwanted exposure. The idea is to find a linear model (a set of weights with no bias) that “explains” the part of the predictions that can be linearly predicted from the features. Then you subtract that part out,\n\n  new_prediction = prediction – (linear model evaluated on features).\n\nThe trick is to choose the weights so that in the resulting residual, the exposure (which here is measured as the correlation or dot‐product between normalized features and the residual prediction) is clipped to a maximum value (a specified threshold). In practice, the exposures are computed by first centering and normalizing both the features and the predictions. Then, for each feature the “exposure” is the dot product between that feature’s normalized values and the normalized residual predictions. The training objective (minimized via gradient descent) is a loss that “penalizes” exposures that exceed the allowed limit. In other words, it finds the best linear correction so that the correlation between the new predictions and each feature is kept within a given range.\n\n──────────────────────────────\nJAX Implementation\n\nBelow is an example implementation using JAX. In this code we:\n\n• Define a helper function (compute_exposures) that centers, normalizes, and then computes all pairwise dot‐products between features and the residual predictions.\n• Define a loss function that, for a given weight vector (our linear model) and inputs, measures how much the exposures exceed a clipped threshold.\n• Run a training loop that uses the gradient of the loss with respect to the weights to update them until the loss is below a given tolerance.\n\nEach code block is commented to explain what is going on.\n\n──────────────────────────────\nCode (using JAX)\n\n------------------------------------------------\n# Start by importing the needed libraries.\nimport jax\nimport jax.numpy as jnp\nfrom jax import grad, jit\nimport numpy as np\n\n# We use a simple gradient descent update\ndef clip_exposures(exposures, max_exp):\n # For each exposure, we want the clip: exposures must lie in [-max_exp, max_exp]\n # This returns the \"target\" exposures as the clipped version of the current exposures.\n return jnp.clip(exposures, -max_exp, max_exp)\n\ndef normalize(x, axis=0, eps=1e-8):\n # Center and then normalize along the given axis.\n x_centered = x - jnp.mean(x, axis=axis, keepdims=True)\n norm = jnp.linalg.norm(x_centered, axis=axis, keepdims=True) + eps\n return x_centered \/ norm\n\ndef compute_exposures(features, residuals):\n \"\"\"\n Given features and residual predictions (both as 2D arrays),\n normalize each column then compute the exposures (dot-product) between each feature\n and the residual. We assume features is of shape (n_samples, n_features)\n and residuals is shape (n_samples, 1).\n \"\"\"\n # Normalize features columnwise\n features_normalized = normalize(features, axis=0)\n # Normalize residuals (only one column)\n residuals_normalized = normalize(residuals, axis=0)\n # Compute exposures: this is equivalent to computing correlations between each feature \n # and the residual predictions.\n # The result is a (n_features x 1) vector.\n exposures = features_normalized.T @ residuals_normalized\n return exposures # Shape: (n_features, 1)\n\ndef loss_fn(weights, features, predictions, max_exp):\n \"\"\"\n Given current weights (the linear model), features, original predictions, and\n maximum allowed exposure, compute the loss.\n\n The linear model is: linear_correction = features.dot(weights)\n So the adjusted prediction is: residual = predictions - linear_correction\n\n We compute the exposures (correlations) between each feature and the residual.\n The target is that each exposure should lie inside [-max_exp, max_exp].\n We penalize exposures beyond that threshold.\n \"\"\"\n # Compute the linear correction\n linear_correction = jnp.dot(features, weights) # shape (n_samples, 1) if weights is (n_features, 1)\n # Compute residual predictions\n residuals = predictions - linear_correction # shape (n_samples, 1)\n # Compute exposures\n expos = compute_exposures(features, residuals) # shape (n_features, 1)\n # Clip exposures: these are the allowed (target) exposures.\n target_expos = clip_exposures(expos, max_exp)\n # We want exposures to be within the allowed band so we penalize the difference.\n # We use ReLU functions to only penalize the spillover above the target.\n # The following computes: sum_i [ relu( abs(expos[i]) - max_exp ) ]\n loss = jnp.sum(jnp.maximum(jnp.abs(expos) - max_exp, 0.0))\n return loss\n\n# JIT compile the loss and gradient function for speed.\nloss_and_grad = jax.jit(jax.value_and_grad(loss_fn))\n\ndef train_jax(features, predictions, max_exp, learning_rate=0.1, max_iters=1000000, tol=1e-7):\n \"\"\"\n Trains the linear model (a simple weight vector with shape (n_features, 1)) \n so that when subtracted from the predictions, the exposures to the features are within\n [-max_exp, max_exp].\n \"\"\"\n n_samples, n_features = features.shape\n # Initialize weights (starting with zeros)\n weights = jnp.zeros((n_features, 1))\n predictions = predictions.reshape(-1, 1) # Ensure (n_samples, 1)\n\n # Training loop\n for i in range(max_iters):\n loss_val, grads = loss_and_grad(weights, features, predictions, max_exp)\n # Gradient descent update\n weights = weights - learning_rate * grads\n if i % 10000 == 0:\n # Print out progress every 10000 iterations.\n print(f\"Iteration {i}, loss: {loss_val:.7f}\")\n if loss_val < tol:\n print(f\"Converged at iteration {i} with loss {loss_val:.7f}\")\n break\n\n # Compute final residual predictions\n linear_correction = jnp.dot(features, weights)\n adjusted_predictions = predictions - linear_correction\n return adjusted_predictions, weights\n\n# Example usage:\nif __name__ == \"__main__\":\n # Create dummy data for demonstration:\n num_samples = 1000\n num_features = 310 # same as the original code's input size\n np.random.seed(42)\n \n # Generate some random features and a random prediction vector.\n features_np = np.random.rand(num_samples, num_features).astype(np.float32)\n predictions_np = np.random.rand(num_samples).astype(np.float32)\n \n # For consistency with the original code, you might want to subtract 0.5 from features.\n features_np = features_np - 0.5\n\n # Convert numpy arrays to jax arrays\n features_jax = jnp.array(features_np)\n predictions_jax = jnp.array(predictions_np)\n\n # Set maximum allowed exposure (for example 0.1)\n max_exposure = 0.1\n\n # Train the model. This returns the neutralized (adjusted) predictions and the linear model weights.\n adjusted_preds, learned_weights = train_jax(features_jax, predictions_jax, max_exposure)\n\n # Optionally, you can check the exposures in the final adjusted predictions:\n final_exposures = compute_exposures(features_jax, adjusted_preds)\n print(\"Final exposures (per feature):\", final_exposures)\n------------------------------------------------\n\n──────────────────────────────\nExplanation of the JAX Code\n\n1. normalize(): This function subtracts the mean from each column (or row if desired) and divides by the norm. This is the first step to compute correlations.\n\n2. compute_exposures(): After normalization, it computes the dot‐product between every feature (column) and the residual predictions. This is analogous to the exposures computed in the TensorFlow code.\n\n3. loss_fn(): Given the current weights (which define the linear correction), the function computes the residual predictions by subtracting the features’ contribution. It then calculates how much each exposure exceeds the maximum allowed (max_exp). We use a simple formulation (sum of positive parts of “excess exceeding max”) as the loss.\n\n4. train_jax(): This is our optimization loop. It starts the weights at zero and then uses gradient descent (with JAX’s automatic differentiation) to adjust the weights until the sum total of exposure exceedance is under a small tolerance.\n\n5. In the main section, we generate dummy data (you would substitute your own data), run the training, and print out the final exposures.\n\n──────────────────────────────\nConclusion\n\nThis JAX-based implementation mirrors the TF\/PyTorch logic presented in your code samples: It finds a linear correction (without a bias) so that when the output of that correction is subtracted from the original predictions the adjusted predictions have exposures to all features below a preset level. Using JAX (with jit-compilation and automatic differentiation) can speed up and simplify the differentiation and training loop."}}],"tstamp":1744155089.2509758472,"metadata":{"token_len":2030,"header_count":{"h1":4,"h2":0,"h3":0,"h4":0,"h5":0,"h6":0},"list_count":{"ordered":5,"unordered":0},"bold_count":{"**":0,"__":2}}} +{"uid":"0e2cc12026e149f5","ans_id":"adBD8UR3aVDUhD6WVLVGKM","model":"o3-mini-2025-01-31","messages":[{"role":"user","content":"how to implement guided process framework . explain with example user registration. implementation of guided process in asp.net core. UI should be single page and users should be guided to add different info of users by highlighting fields on page and showing info how about field and how step can be completed and only keeping that field enabled and rest page inaccessible"},{"role":"assistant","content":{"answer":"Below is one way to implement a guided (wizard‐style) process for user registration in an ASP.NET Core application. In our example, the registration page is a single page where only the “current” field is active and highlighted, while the other fields are disabled until the user completes the current step. You also show context–sensitive help (for example as a tooltip or an info box) that explains how to fill in that step, and then move to the next field once the input is valid.\n\nThe solution consists of three parts:\n\n• Backend (ASP.NET Core Controller\/Model) \n• Razor view (or HTML page) with the complete form \n• JavaScript and CSS to “guide” the user (enable only the current field, highlight it, and show help)\n\nBelow is a sample implementation.\n\n──────────────────────────────\n1. Create an ASP.NET Core Project and User Model\n\nYou can use the ASP.NET Core MVC template. Define a user registration model. For example, create a file named Models\/UserRegistration.cs:\n\n------------------------------------------------\nnamespace MyApp.Models\n{\n public class UserRegistration\n {\n public string FullName { get; set; }\n public string Email { get; set; }\n public string Password { get; set; }\n \/\/ Add additional fields as needed.\n }\n}\n------------------------------------------------\n\n──────────────────────────────\n2. Create the Registration Controller\n\nCreate a controller that returns the registration view and handles form submission. For instance, add Controllers\/RegistrationController.cs:\n\n------------------------------------------------\nusing Microsoft.AspNetCore.Mvc;\nusing MyApp.Models;\n\nnamespace MyApp.Controllers\n{\n public class RegistrationController : Controller\n {\n \/\/ GET: \/Registration\/\n public IActionResult Index()\n {\n return View(new UserRegistration());\n }\n\n \/\/ POST: \/Registration\/Submit\n [HttpPost]\n public IActionResult Submit(UserRegistration model)\n {\n if (ModelState.IsValid)\n {\n \/\/ Save registration info to the database or do further processing.\n return RedirectToAction(\"Success\");\n }\n return View(\"Index\", model);\n }\n\n public IActionResult Success()\n {\n return View();\n }\n }\n}\n------------------------------------------------\n\n──────────────────────────────\n3. Create the Single-Page Guided Registration View\n\nIn Views\/Registration\/Index.cshtml, build a form that contains all steps of the registration. Only one input field is enabled at a time; the others are disabled. You can also add a help message area (or use tooltips\/popovers) that shows the instruction for the current field.\n\nExample using Razor, HTML, JavaScript, and some CSS:\n\n------------------------------------------------\n@model MyApp.Models.UserRegistration\n@{\n ViewData[\"Title\"] = \"User Registration\";\n}\n\n