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未来预测MT4指标—Projections技术解析

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标题:“Projections Future Winnning_indicator_mt4_Projections_”描述了这款MT4指标的用途,即用于外汇市场分析和交易决策。该指标的名称“Projections Future Winnning”暗示它可能与预测未来价格走势相关,而“_mt4”后缀表明它是专为MetaTrader 4(MT4)平台设计的。MT4是全球交易者广泛使用的外汇交易平台,它支持用户使用自定义指标来扩展其交易平台的功能。 描述中的“PROJECTIONS FUTURE MT4 INDICATOR”进一步明确了指标的功能,它是一个专门设计用于MetaTrader 4交易平台的预测指标。尽管描述中并没有提供关于这个指标如何工作的详细信息,但通常这类指标会使用历史数据和数学算法来预测未来的市场行为或价格变化,从而帮助交易者做出更为明智的交易决策。 标签“indicator mt4 Projections”指明了这个指标所属的类别,即它是MT4平台上的一个指标,并且专注于提供价格走势的预测信息。这表明该指标可能集成了图表分析工具,包括支撑/阻力水平、趋势线、移动平均线、斐波那契回撤以及可能的预测模型,如时间序列预测、回归分析、人工智能技术(如神经网络)等。 文件名称列表中的“Projections Future Winnning”可能是一个不完整或被截断的文件名。这个列表可能是在提及相关MT4指标时,在压缩包内所包含的文件名称。通常情况下,这个文件应该包含指标的主要文件(例如.EX4或.MQ4文件),可能还包括指标的说明文档、设置指南或者示例图表,有时也可能包含源代码文件(.mqh)。 综合以上信息,可以推测这个名为“Projections Future Winnning”的MT4指标,可能具有以下技术特点和功能: 1. 预测功能:可能运用统计学、概率论或更高级的数学模型,预测未来市场趋势或价格变动,为交易决策提供数据支持。 2. 交易信号:指标可能包括发出买入或卖出信号的功能,这些信号可能基于预设的条件或算法,以图形或警报的形式表现出来。 3. 自定义设置:该指标可能允许用户根据自己的交易策略和偏好来调整设置,例如选择不同的预测模型、调整敏感度等。 4. 兼容性:作为MT4平台上的指标,它应当兼容各种MetaTrader 4平台的版本,包括个人电脑版本和移动版本。 5. 易于理解:该指标应当提供清晰直观的图表表现,使用户可以容易理解其预测结果和指标信号。 6. 多货币对和市场:虽然该指标特指外汇市场,但一般情况下,MT4指标能够应用于多种货币对和市场,例如股票、商品、指数等。 7. 实时分析:MT4平台的指标一般都支持实时数据分析,该指标应该也不例外,可以即时反映市场变化并提供相应的分析。 8. 研究与支持:由于指标可能具有一定的复杂性,它可能包含一个简要的手册或用户指南,帮助用户学习如何使用它。而且,可能会有相关的技术支撑,如在线帮助文档、论坛讨论、官方维护等。 了解这些知识点可以帮助潜在用户评估这个MT4指标是否符合他们的需求,并且了解如何在MT4平台上实现其功能。在实际购买或下载之前,建议潜在用户仔细阅读完整的功能介绍、用户反馈和评价,以获得更全面的使用体验信息。

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Multi-Headed Attention Layer (MLA). Attributes: dim (int): Dimensionality of the input features. n_heads (int): Number of attention heads. n_local_heads (int): Number of local attention heads for distributed systems. q_lora_rank (int): Rank for low-rank query projection. kv_lora_rank (int): Rank for low-rank key/value projection. qk_nope_head_dim (int): Dimensionality of non-positional query/key projections. qk_rope_head_dim (int): Dimensionality of rotary-positional query/key projections. qk_head_dim (int): Total dimensionality of query/key projections. v_head_dim (int): Dimensionality of value projections. softmax_scale (float): Scaling factor for softmax in attention computation. """ def __init__(self, args: ModelArgs): super().__init__() self.dim = args.dim self.n_heads = args.n_heads self.n_local_heads = args.n_heads // world_size self.q_lora_rank = args.q_lora_rank self.kv_lora_rank = args.kv_lora_rank self.qk_nope_head_dim = args.qk_nope_head_dim self.qk_rope_head_dim = args.qk_rope_head_dim self.qk_head_dim = args.qk_nope_head_dim + args.qk_rope_head_dim self.v_head_dim = args.v_head_dim if self.q_lora_rank == 0: self.wq = ColumnParallelLinear(self.dim, self.n_heads * self.qk_head_dim) else: self.wq_a = Linear(self.dim, self.q_lora_rank) self.q_norm = RMSNorm(self.q_lora_rank) self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.qk_head_dim) self.wkv_a = Linear(self.dim, self.kv_lora_rank + self.qk_rope_head_dim) self.kv_norm = RMSNorm(self.kv_lora_rank) self.wkv_b = ColumnParallelLinear(self.kv_lora_rank, self.n_heads * (self.qk_nope_head_dim + self.v_head_dim)) self.wo = RowParallelLinear(self.n_heads * self.v_head_dim, self.dim) self.softmax_scale = self.qk_head_dim ** -0.5 if args.max_seq_len > args.original_seq_len: mscale = 0.1 * args.mscale * math.log(args.rope_factor) + 1.0 self.softmax_scale = self.softmax_scale * mscale * mscale if attn_impl == "naive": self.register_buffer("k_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.n_local_heads, self.qk_head_dim), persistent=False) self.register_buffer("v_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.n_local_heads, self.v_head_dim), persistent=False) else: self.register_buffer("kv_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.kv_lora_rank), persistent=False) self.register_buffer("pe_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.qk_rope_head_dim), persistent=False)

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``` def run(parser): args = parser.parse_args() print('Processing scan {}.'.format(args.scan_id)) # Load projections and read out geometry data from the DICOM header. raw_projections, parser = read_dicom(parser) args = parser.parse_args() if args.save_all: save_path = Path(args.path_out) / Path('{}_curved_helix_projections.tif'.format(args.scan_id)) save_to_tiff_stack_with_metadata(raw_projections, save_path, metadata=vars(args)) # Rebin helical projections from curved detector to flat detector. # Step can be skipped if the reconstruction supports curved detectors. if args.no_multiprocessing: proj_flat_detector = rebin_curved_to_flat_detector(args, raw_projections) else: location = 'tmp/cache_dir' # Todo: '/home/fabian/Desktop/tmp/cache_dir' memory = Memory(location, verbose=0) cached_rebin_curved_to_flat_detector_core = memory.cache(_rebin_curved_to_flat_detector_core) data = (args, raw_projections) proj_flat_detector = np.array(Parallel(n_jobs=8)( delayed(rebin_curved_to_flat_detector_multiprocessing)(data, col) for col in tqdm.tqdm(range(data[1].shape[0]), 'Rebin curved to flat detector'))) if args.save_all: save_path = Path(args.path_out) / Path('{}_flat_helix_projections.tif'.format(args.scan_id)) save_to_tiff_stack_with_metadata(proj_flat_detector, save_path, metadata=vars(args)) # Rebinning of projections acquired on a helical trajectory to full-scan (2pi) fan beam projections. proj_fan_geometry = rebin_helical_to_fan_beam_trajectory(args, proj_flat_detector) save_path = Path(args.path_out) / Path('{}_flat_fan_projections.tif'.format(args.scan_id)) save_to_tiff_stack_with_metadata(proj_fan_geometry, save_path, metadata=vars(args)) print('Finished. Results saved at {}.'.format(save_path.resolve())) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--path_dicom', type=str, required=True, help='Local path of helical projection data.') parser.add_argument('--path_out', type=str, default='out', help='Output path of rebinned data.') parser.add_argument('--scan_id', type=str, default='scan_001', help='Custom scan ID.') parser.add_argument('--idx_proj_start', type=int, default=12000, help='First index of helical projections that are processed.') parser.add_argument('--idx_proj_stop', type=int, default=16000, help='Last index of helical projections that are processed.') parser.add_argument('--save_all', dest='save_all', action='store_true', help='Save all intermediate results.') parser.add_argument('--no_multiprocessing', dest='no_multiprocessing', action='store_true', help='Switch off multiprocessing using joblib.') run(parser)```帮我解释代码内容

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``` parser = argparse.ArgumentParser() parser.add_argument('--path_dicom', type=str, required=True, help='Local path of helical projection data.') parser.add_argument('--path_out', type=str, default='out', help='Output path of rebinned data.') parser.add_argument('--scan_id', type=str, default='scan_001', help='Custom scan ID.') parser.add_argument('--idx_proj_start', type=int, default=12000, help='First index of helical projections that are processed.') parser.add_argument('--idx_proj_stop', type=int, default=16000, help='Last index of helical projections that are processed.') parser.add_argument('--save_all', dest='save_all', action='store_true', help='Save all intermediate results.') parser.add_argument('--no_multiprocessing', dest='no_multiprocessing', action='store_true', help='Switch off multiprocessing using joblib.') run(parser)```PS E:\Desktop\helix2fan> python "E:\Desktop\helix2fan\helix2fan-master\main.py" --path_dicom E:\DDDM-DATA\manifest-1586193031612\NSCLC-Radiomics\LUNG1-001\09-18-2008-StudyID-NA-69331\0.000000-NA-82046\1-001.dcm Processing scan scan_001. Traceback (most recent call last): File "E:\Desktop\helix2fan\helix2fan-master\main.py", line 68, in <module> run(parser) File "E:\Desktop\helix2fan\helix2fan-master\main.py", line 19, in run raw_projections, parser = read_dicom(parser) File "E:\Desktop\helix2fan\helix2fan-master\read_data.py", line 73, in read_dicom data_headers, raw_projections = read_projections(args.path_dicom, indices) File "E:\Desktop\helix2fan\helix2fan-master\read_data.py", line 23, in read_projections file_names = sorted([f for f in os.listdir(folder) if f.endswith(".dcm")]) NotADirectoryError: [WinError 267] 目录名称无效。: 'E:\\DDDM-DATA\\manifest-1586193031612\\NSCLC-Radiomics\\LUNG1-001\\09-18-2008-StudyID-NA-69331\\0.000000-NA-82046\\1-001.dcm'

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from wordcloud import WordCloud # wordcloud是包名,WordCloud是类名 txt = "这是我写的第一个词云图" wordcloud = WordCloud( background_color="white", width=800, height=600, max_words=50).generate(txt) #生成图片 image = wordcloud.to_image() #展示图片 image.show() 显示E:\Python\pythonProject1\.venv\Scripts\python.exe E:\Python\pythonProject1\练习\1-1.py Traceback (most recent call last): File "E:\Python\pythonProject1\练习\1-1.py", line 18, in <module> wordcloud = WordCloud( ^^^^^^^^^^ File "E:\Python\pythonProject1\.venv\Lib\site-packages\wordcloud\wordcloud.py", line 337, in __init__ self.color_func = color_func or colormap_color_func(colormap) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Python\pythonProject1\.venv\Lib\site-packages\wordcloud\wordcloud.py", line 105, in __init__ import matplotlib.pyplot as plt File "E:\Python\pythonProject1\.venv\Lib\site-packages\matplotlib\pyplot.py", line 70, in <module> from matplotlib.figure import Figure, FigureBase, figaspect File "E:\Python\pythonProject1\.venv\Lib\site-packages\matplotlib\figure.py", line 40, in <module> from matplotlib import _blocking_input, backend_bases, _docstring, projections File "E:\Python\pythonProject1\.venv\Lib\site-packages\matplotlib\projections\__init__.py", line 55, in <module> from .. import axes, _docstring File "E:\Python\pythonProject1\.venv\Lib\site-packages\matplotlib\axes\__init__.py", line 2, in <module> from ._axes import Axes File "E:\Python\pythonProject1\.venv\Lib\site-packages\matplotlib\axes\_axes.py", line 11, in <module> import matplotlib.category # Register category unit converter as side effect. ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Python\pythonProject1\.venv\Lib\site-packages\matplotlib\category.py", line 14, in <module> import dateutil.parser File "E:\Python\pythonProject1\.venv\Lib\site-packages\dateutil\parser\__