import numpy as np import cv2 def ComputeMinLevel(hist, pnum): index = np.add.accumulate(hist) return np.argwhere(index>pnum * 8.3 * 0.01)[0][0] def ComputeMaxLevel(hist, pnum): hist_0 = hist[::-1] Iter_sum = np.add.accumulate(hist_0) index = np.argwhere(Iter_sum > (pnum * 2.2 * 0.01))[0][0] return 255-index def LinearMap(minlevel, maxlevel): if (minlevel >= maxlevel): return [] else: index = np.array(list(range(256))) screenNum = np.where(index<minlevel,0,index) screenNum = np.where(screenNum> maxlevel,255,screenNum) for i in range(len(screenNum)): if screenNum[i]> 0 and screenNum[i] < 255: screenNum[i] = (i - minlevel) / (maxlevel - minlevel) * 255 return screenNum def CreateNewImg(img): h, w, d = img.shape newimg = np.zeros([h, w, d]) for i in range(d): imghist = np.bincount(img[:, :, i].reshape(1, -1)[0]) minlevel = ComputeMinLevel(imghist, h * w) maxlevel = ComputeMaxLevel(imghist, h * w) screenNum = LinearMap(minlevel, maxlevel) if (screenNum.size == 0): continue for j in range(h): newimg[j, :, i] = screenNum[img[j, :, i]] return newimg if __name__ == '__main__': img = cv2.imread('D:\shujuji\wu\\Image_20230225221250865.jpg') newimg = CreateNewImg(img) cv2.imshow('original_img', img) cv2.imshow('new_img', newimg / 200) cv2.waitKey(0) cv2.destroyAllWindows()修改调整图片大小

python 3.74 运行import numpy as np 报错lib\site-packages\numpy\init.py

import numpy as np import os,sys #获取当前文件夹，并根据文件名 def path(fileName): p=sys.path[0]+'\\'+fileName return p #读文件 def readFile(fileName): f=open(path(fileName)) str=f.read() ...

Python Numpy:找到list中的np.nan值方法

import numpy as np x = np.array([2, 3, np.nan, 5, np.nan, 5, 2, 3]) # 简单查找np.nan值 for item in x: if np.isnan(item): print('yes') 在这个例子中，np.isnan(item)函数被用来遍历数组x的每个...

# 定义获得时序最大值坐标的函数 def TSMaxIndex(array, first = True): array = np.array(array).flatten() if first: return np.argmax(array) else: max_index = np.argwhere(array == np.amax(array)).flatten() return max_index[-1]

TSMaxIndex函数是一个用于获得时序最大值坐标的函数。...如果指定返回最后一个最大值的索引（first参数为False），则使用numpy的argwhere方法找到所有最大值的索引，然后返回最后一个最大值的索引。

优化这段代码 from pyecharts import options as opts from pyecharts.charts import Bar import pyecharts.charts as pyec import numpy as np hist,bin_edges = np.histogram(df['电影评分'], bins = 13) bar = ( Bar() .add_xaxis([str(x) for x in bin_edges[:-1]]) .add_yaxis("评分分布", [float(x) for x in hist], category_gap = 5) .set_global_opts( title_opts = opts.TitleOpts(title = '豆瓣电影TOP250-电影评分分布-直方图', pos_left = "center"), legend_opts = opts.LegendOpts(is_show = False) ) ) bar.render_notebook()

import numpy as np def get_data(df): hist, bin_edges = np.histogram(df['电影评分'], bins=13) x_data = [str(x) for x in bin_edges[:-1]] y_data = [float(x) for x in hist] return x_data, y_data def...

import cv2 import os import numpy as np from sklearn import svm import joblib def read_images(folder): images = [] labels = [] for filename in os.listdir(folder): label = filename.split('.')[0] img = cv2.imread(os.path.join(folder,filename)) if img is not None: images.append(img) labels.append(label) return images, labels # 提取特征向量 def extract_features(images): features = [] for img in images: gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) hist = cv2.calcHist([gray],[0],None,[256],[0,256]) features.append(hist.flatten()) return features # 读取图像和标签 images, labels = read_images('C:/Users/Administrator/Desktop/111') # 提取特征向量 features = extract_features(images) # 训练模型 clf = svm.SVC() clf.fit(features, labels) # 保存模型到文件 joblib.dump(clf, 'model.pkl') clf = joblib.load('E:/xiangmu/measure/model.pkl') print(clf) # 预测新图像 img = cv2.imread('C:/Users/Administrator/Desktop/dc9cd0a3a572635fb27973627f514dfb.jpeg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) hist = cv2.calcHist([gray],[0],None,[256],[0,256]) features = np.array(hist.flatten()).reshape(1, -1) print(hist) label = clf.predict(features)[0] print('识别结果：', label) 帮我优化代码，可以打印概率

import numpy as np from sklearn import svm from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_split from skimage.io import imread from skimage.transform import ...

import pandas as pdimport numpy as npimport talibimport tushare as ts# 先写出回测框架class Backtest(): def init(self, data, init_balance): self.data = data self.init_balance = init_balance self.position = 0 self.balance = init_balance self.equity = 0 def update_balance(self, price): self.equity = self.position * price self.balance = self.balance + self.equity def run(self, strategy): for i in range(1, len(self.data)): signal = strategy.generate_signal(self.data.iloc[:i, :]) price = self.data.iloc[i, 0] # 按照信号来调整持仓 if signal == 1: self.position = np.floor(self.balance / price) # 买入所有可用资金 elif signal == -1: self.position = 0 # 卖出所有股票 self.update_balance(price) print("日期:", self.data.index[i], "价格:", price, "信号:", signal, "账户价值:", self.balance) # 输出最后的回测结果 print("回测结果: 最开始的账户余额为", self.init_balance, "，最终的账户余额为", self.balance, "，因此您的盈亏为", self.balance-self.init_balance)# 再写出策略类class MACD_Strategy(): def init(self, fast_period, slow_period, signal_period): self.fast_period = fast_period self.slow_period = slow_period self.signal_period = signal_period def generate_signal(self, data): macd, signal, hist = talib.MACD(data["close"], fastperiod=self.fast_period, slowperiod=self.slow_period, signalperiod=self.signal_period) if hist[-1] > 0 and hist[-2] < 0: return 1 # 金叉，买入 elif hist[-1] < 0 and hist[-2] > 0: return -1 # 死叉，卖出 else: return 0 # 无操作# 最后的主程序if name == "main": # 下载数据 data = ts.get_hist_data("600000", start="2020-01-01", end="2021-01-01") data = data.sort_index() # 按日期排序 data = data.loc[:, ["open", "high", "close", "low", "volume"]] # 只保留这五列 data.index = pd.to_datetime(data.index) # 初始化回测 backtest = Backtest(data, init_balance=100000) # 初始化策略 strategy = MACD_Strategy(fast_period=12, slow_period=26, signal_period=9) # 运行回测 backtest.run(strategy)

这是一段Python代码，用于导入Pandas、Numpy、Talib以及Tushare这几个Python库。Pandas和Numpy是用于数据处理的库，Talib是用于技术指标计算的库，Tushare是用于获取股市数据的库。

import cv2 import numpy as np def get_largest_connected_component_points(mask): # 连通域标记 _, labels, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8) # 获取连通域的面积 areas = stats[:, cv2.CC_STAT_AREA] # 找到面积最大的连通域的标签 largest_label = np.argmax(areas[1:]) + 1 # 提取连通域的坐标点 points = np.argwhere(labels == largest_label) return points

你提供的代码函数get_largest_connected_component_points通过cv2.connectedComponentsWithStats函数找到图像中的连通域，并获取连通域的面积。然后，它找到面积最大的连通域的标签，并使用np.argwhere函数提取该...

这个代码有什么问题import cv2 import numpy as np img = cv2.imread(r'E:\\postgraduate\\three\\DIP3E_Original_Images_CH03\\1.tif', 0) def invert_image(img): height, width = img.shape Image = np.zeros((height, width), np.uint8) for i in range(0, height): for j in range(0, width): imgPixel = img[i, j] Image[i, j] = 255 - imgPixel return Image Image = invert_image(img) cv2.namedWindow('Image1') cv2.imshow('Image1', Image) cv2.waitKey(0) cv2.destroyAllWindows()

import numpy as np img = cv2.imread('E:/postgraduate/three/DIP3E_Original_Images_CH03/1.tif', 0) def invert_image(img): height, width = img.shape Image = np.zeros((height, width), np.uint8) for i...

import seaborn as sns import numpy as np np.random.seed(123) # a、服从μ=0,σ=1 的正态分布： data=np.random.randn(10000) # data sns.distplot(data) # b、服从μ=loc,σ=scale 的正态分布： #loc:正太分布的均值 scale:正太分布的标准差 size:设定数组形状 data2=np.random.normal(loc=2, scale=1, size=10000) data2 sns.distplot(data2)这段代码用matplotlib绘制

import numpy as np # 设置主题 plt.style.use('ggplot') # 生成数据 np.random.seed(123) data1 = np.random.randn(10000) data2 = np.random.normal(loc=2, scale=1, size=10000) # 绘制直方图 plt.hist(data1,...

import matplotlib.pyplot as plt import numpy as np x1 = np.random.randn(100) x2 = np.random.randn

import numpy as np是导入numpy库，并将其重命名为np，numpy是一个用于进行科学计算的库，提供了很多数学函数和数组操作的功能。 x1 = np.random.randn(100)是使用numpy库中的random模块生成一个包含100个随机数的...

python # 导入第三方库 import numpy as np import matplotlib.pyplot as plt # 数据处理模块 def load_data(file_path): data = np.loadtxt(file_path) return data # 统计分析模块 def calculate_statistics(data): mean = np.mean(data) std = np.std(data) return mean, std # 可视化模块 def plot_histogram(data): plt.hist(data, bins=10) plt.xlabel('Value') plt.ylabel('Frequency') plt.title('Histogram') plt.show() def plot_boxplot(data): plt.boxplot(data) plt.xlabel('Data') plt.ylabel('Value') plt.title('Boxplot') plt.show() # 主程序流程 if name == 'main': file_path = 'data.txt' data = load_data(file_path) mean, std = calculate_statistics(data) print('Mean:', mean) print('Standard deviation:', std) plot_histogram(data) plot_boxplot(data) 2

首先，它导入了 numpy 和 matplotlib.pyplot 两个库，用于数据处理和可视化。然后，定义了三个函数：load_data 函数用于从文件中读取数据，calculate_statistics 函数计算数据的均值和标准差，plot_histogram 和 ...

请给下面代码添加注释 import numpy as npdef least_squares(X, y): X = np.array(X) y = np.array(y) w = np.linalg.inv(X.T @ X) @ X.T @ y return wX = np.array([[1, 2], [1, 3], [1, 4]])y = np.array([2, 3, 4])w = least_squares(X, y)print(w)

import numpy as np # 定义最小二乘法函数 def least_squares(X, y): # 将 X 和 y 转换成 numpy 数组 X = np.array(X) y = np.array(y) # 使用矩阵运算计算最小二乘法系数 w = np.linalg.inv(X.T @ X) @ X.T...

import numpy as np def generate_matrix(a): arr = np.zeros((a,a)) for i in range(a): arr[i,:i+1] = np.arange(1,i+2) return arr a = 5 matrix = generate_matrix(a) #print(matrix) col_sum = np.sum(matrix, axis=0) print(col_sum)怎么在输出中输出逗号

import numpy as np def generate_matrix(a): arr = np.zeros((a,a)) for i in range(a): arr[i,:i+1] = np.arange(1,i+2) return arr a = 5 matrix = generate_matrix(a) col_sum = np.sum(matrix, axis=0...

import numpy as np import pandas as pd from itertools import product def doe(factors): loopval = [] df_col = [] values = [] for i in factors.keys(): df_col.append(i) loopval.append(factors[i]) for i in product(*loopval): values.append(list(i)) df = pd.DataFrame(values,columns=df_col) df_col.append("标准序") df["标准序"] = df.index.copy() + 1 df = df.reindex(np.random.permutation(df.index)).reset_index(drop=True) return df 优化这段代码并提高性能

import numpy as np import pandas as pd def doe(factors): df_col = list(factors.keys()) values = np.array(np.meshgrid(*factors.values())).T.reshape(-1, len(factors)) df = pd.DataFrame(values, ...

怎么把代码的图片保存下来？ import matplotlib.pyplot as plt plt.style.use('seaborn-whitegrid') import pandas as pd import numpy as np epsilon = 100 vals_laplace = [np.random.laplace(loc=0, scale=1/epsilon) for x in range(100000)] delta = 1e-5 sigma = np.sqrt(2 * np.log(1.25 / delta)) * 1 / epsilon vals_gauss = [np.random.normal(loc=0, scale=sigma) for x in range(100000)] plt.hist(vals_laplace, bins=50, label='Laplace') plt.hist(vals_gauss, bins=50, alpha=.7, label='Gaussian') plt.legend() plt.show()

你可以使用matplotlib的savefig函数将代码中的图片保存到本地文件系统中。将plt.show()替换为以下代码即可： plt.savefig('histogram.png') 这会将生成的图片保存到当前工作目录下的histogram.png...

对下列代码进行注释：import numpy as npdef euclidean(x, y): return np.sqrt(np.sum((x - y)**2))

import numpy as np # 定义欧几里得距离函数，输入参数为x和y两个数组 def euclidean(x, y): # 计算x和y两个数组差的平方，并使用np.sum函数进行求和 # 注意这里的**2表示对差值进行平方的操作 # np.sum函数会对...

import argparse import logging import re from multiprocessing import Process, Queue from pathlib import Path import numpy as np from skimage import exposure, filters from modules.config import logger from modules.volume import volume_loading_func, volume_saving_func def normalize_intensity( np_volume: np.ndarray, relative_path: Path, logger: logging.Logger ): logger.info(f"[processing start] {relative_path}") nstack = len(np_volume) stack: np.ndarray = np_volume[nstack // 2 - 16 : nstack // 2 + 16] hist_y, hist_x = exposure.histogram(stack[stack > 0]) thr = filters.threshold_otsu(stack[stack > 0]) peak_air = np.argmax(hist_y[hist_x < thr]) + hist_x[0] peak_soil = np.argmax(hist_y[hist_x > thr]) + (thr - hist_x[0]) + hist_x[0] np_volume = np_volume.astype(np.int64) for i in range(len(np_volume)): np_volume[i] = ( (np_volume[i] - peak_air).clip(0) / (peak_soil - peak_air) * 256 / 2 ) logger.info(f"[processing end] {relative_path}") return exposure.rescale_intensity( np_volume, in_range=(0, 255), out_range=(0, 255) ).astype(np.uint8) 请详细解释每一行的代码意思

import numpy as np # 用来处理数组数据 from skimage import exposure, filters # 用来进行图像处理 from modules.config import logger # 从 modules.config 模块中导入日志记录器 from modules.volume import ...

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请给下面代码添加注释 import numpy as npdef least_squares(X, y): X = np.array(X) y = np.array(y) w = np.linalg.inv(X.T @ X) @ X.T @ y return wX = np.array([[1, 2], [1, 3], [1, 4]])y = np.array([2, 3, 4])w = least_squares(X, y)print(w)

import numpy as np def generate_matrix(a): arr = np.zeros((a,a)) for i in range(a): arr[i,:i+1] = np.arange(1,i+2) return arr a = 5 matrix = generate_matrix(a) #print(matrix) col_sum = np.sum(matrix, axis=0) print(col_sum)怎么在输出中输出逗号

对下列代码进行注释：import numpy as npdef euclidean(x, y): return np.sqrt(np.sum((x - y)**2))

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