SET_Time算法试验运行结果分析

版权申诉
0 下载量 140 浏览量 更新于2024-10-03 收藏 2KB RAR 举报
资源摘要信息:"SET_Time算法试验运行结果" SET_Time算法是一种非常有效的算法,主要用于解决特定的问题或者优化特定的系统性能。从描述中我们可以看出,这是该算法的一个试验运行结果。试验运行是算法开发过程中的一个重要环节,它可以帮助开发者了解算法的实际运行情况,包括运行时间、效率、稳定性等方面,以便对算法进行进一步的优化和改进。 在这次试验运行中,开发者选择了"SET_Time"这一算法进行测试。我们可以猜测,这个算法可能与时间相关,比如可能用于优化某个需要频繁计算时间的系统,或者用于预测某个事件的发生时间等。由于具体的应用场景没有在描述中给出,我们无法确定算法的具体功能。 "Trial Run"标签表示这是一个试验性的运行,也就是说,这次运行的目的是为了测试和验证,而不是用于生产环境。这可以帮助开发者在实际部署之前,发现并解决可能存在的问题。 由于文件中只提供了一个文件名"SET_Output_1",我们无法获得更多的信息。但是,我们可以推测,这个文件可能包含了SET_Time算法试验运行的输出结果,包括运行时间、运行结果、错误信息等。这将为开发者提供了宝贵的数据,帮助他们了解算法在实际运行中的表现。 总的来说,这个文件提供了一个非常有效的算法的试验运行结果。虽然我们无法从这个文件中获得更多的信息,但是我们可以了解到,这是一个试验性的运行,目的是为了测试和验证算法的实际性能。希望开发者能够通过这次试验运行,对算法进行进一步的优化和改进。

解释根据给出的代码,可以将其转化为以下CMake代码: 复制 cmake_minimum_required(VERSION 3.5) project(UavRectifyLoadLIb LANGUAGES CXX) set(CMAKE_CXX_STANDARD 11) set(CMAKE_CXX_STANDARD_REQUIRED ON) set(CMAKE_AUTOMOC ON) set(CMAKE_AUTORCC ON) set(CMAKE_AUTOUIC ON) find_package(Qt5Core REQUIRED) add_executable(UavRectifyLoadLIb main.cpp ) target_link_libraries(UavRectifyLoadLIb PRIVATE Qt5::Core UAVAutoRectifyMt UAVAutoRectify UAVAutoRectifyFi DEMDriver Projection IImage_gC opencv_core opencv_highgui opencv_imgproc opencv_features2d opencv_imgcodecs ) target_include_directories(UavRectifyLoadLIb PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../../../include/gdal1101 ${CMAKE_CURRENT_SOURCE_DIR}/../include ${CMAKE_CURRENT_SOURCE_DIR}/../../../lib/opencvf249 ${CMAKE_CURRENT_SOURCE_DIR}/../../../../../../../usr/local/include ) if(UNIX AND NOT APPLE) target_link_directories(UavRectifyLoadLIb PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../../../product/release32 ${CMAKE_CURRENT_SOURCE_DIR}/../../../../../../../usr/local/lib ) endif() if(WIN32) if(CMAKE_BUILD_TYPE STREQUAL "Debug") set_target_properties(UavRectifyLoadLIb PROPERTIES RUNTIME_OUTPUT_DIRECTORY_DEBUG ${CMAKE_CURRENT_SOURCE_DIR}/../../../../RasterManager/bin/Debug ) else() set_target_properties(UavRectifyLoadLIb PROPERTIES RUNTIME_OUTPUT_DIRECTORY_RELEASE ${CMAKE_CURRENT_SOURCE_DIR}/../../../../RasterManager/bin/release ) endif() else() if(CMAKE_BUILD_TYPE STREQUAL "Debug") set_target_properties(UavRectifyLoadLIb PROPERTIES RUNTIME_OUTPUT_DIRECTORY_DEBUG ${CMAKE_CURRENT_SOURCE_DIR}/../../../product/release32 ) else() set_target_properties(UavRectifyLoadLIb PROPERTIES RUNTIME_OUTPUT_DIRECTORY_RELEASE ${CMAKE_CURRENT_SOURCE_DIR}/../../../product/release32 ) endif() endif()

2023-06-11 上传

def draw_stats(self, vals, vals1, vals2, vals3, vals4, vals5, vals6): self.ax1 = plt.subplot(self.gs[0, 0]) self.ax1.plot(vals) self.ax1.set_xlim(self.xlim) locs = self.ax1.get_xticks() locs[0] = self.xlim[0] locs[-1] = self.xlim[1] self.ax1.set_xticks(locs) self.ax1.use_sticky_edges = False self.ax1.set_title(f'Connected Clients Ratio') self.ax2 = plt.subplot(self.gs[1, 0]) self.ax2.plot(vals1) self.ax2.set_xlim(self.xlim) self.ax2.set_xticks(locs) self.ax2.yaxis.set_major_formatter(FuncFormatter(format_bps)) self.ax2.use_sticky_edges = False self.ax2.set_title('Total Bandwidth Usage') self.ax3 = plt.subplot(self.gs[2, 0]) self.ax3.plot(vals2) self.ax3.set_xlim(self.xlim) self.ax3.set_xticks(locs) self.ax3.use_sticky_edges = False self.ax3.set_title('Bandwidth Usage Ratio in Slices (Averaged)') self.ax4 = plt.subplot(self.gs[3, 0]) self.ax4.plot(vals3) self.ax4.set_xlim(self.xlim) self.ax4.set_xticks(locs) self.ax4.use_sticky_edges = False self.ax4.set_title('Client Count Ratio per Slice') self.ax5 = plt.subplot(self.gs[0, 1]) self.ax5.plot(vals4) self.ax5.set_xlim(self.xlim) self.ax5.set_xticks(locs) self.ax5.use_sticky_edges = False self.ax5.set_title('Coverage Ratio') self.ax6 = plt.subplot(self.gs[1, 1]) self.ax6.plot(vals5) self.ax6.set_xlim(self.xlim) self.ax6.set_xticks(locs) self.ax6.yaxis.set_major_formatter(FormatStrFormatter('%.3f')) self.ax6.use_sticky_edges = False self.ax6.set_title('Block ratio') self.ax7 = plt.subplot(self.gs[2, 1]) self.ax7.plot(vals6) self.ax7.set_xlim(self.xlim) self.ax7.set_xticks(locs) self.ax7.yaxis.set_major_formatter(FormatStrFormatter('%.3f')) self.ax7.use_sticky_edges = False self.ax7.set_title('Handover ratio')修改为一张张输出图片

2023-06-02 上传

import pandas as pd import warnings import sklearn.datasets import sklearn.linear_model import matplotlib import matplotlib.font_manager as fm import matplotlib.pyplot as plt import numpy as np import seaborn as sns data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx') print(data.info()) fig = plt.figure(figsize=(10, 8)) sns.heatmap(data.corr(), cmap="YlGnBu", annot=True) plt.title('相关性分析热力图') plt.rcParams['axes.unicode_minus'] = False plt.rcParams['font.sans-serif'] = 'SimHei' plt.show() y = data['y'] x = data.drop(['y'], axis=1) print('************************输出新的特征集数据***************************') print(x.head()) from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42) def relu(x): output=np.maximum(0, x) return output def relu_back_propagation(derror_wrt_output,x): derror_wrt_dinputs = np.array(derror_wrt_output, copy=True) derror_wrt_dinputs[x <= 0] = 0 return derror_wrt_dinputs def activated(activation_choose,x): if activation_choose == 'relu': return relu(x) def activated_back_propagation(activation_choose, derror_wrt_output, output): if activation_choose == 'relu': return relu_back_propagation(derror_wrt_output, output) class NeuralNetwork: def __init__(self, layers_strcuture, print_cost = False): self.layers_strcuture = layers_strcuture self.layers_num = len(layers_strcuture) self.param_layers_num = self.layers_num - 1 self.learning_rate = 0.0618 self.num_iterations = 2000 self.x = None self.y = None self.w = dict() self.b = dict() self.costs = [] self.print_cost = print_cost self.init_w_and_b() def set_learning_rate(self,learning_rate): self.learning_rate=learning_rate def set_num_iterations(self, num_iterations): self.num_iterations = num_iterations def set_xy(self, input, expected_output): self.x = input self.y = expected_output

2023-06-07 上传