dpi = 80 #inchpath_jpg=f"F:\\kaggleDataSet\\diabeticRetinopathy\\resized_train_cropped\\18017_left.jpeg" # too many vessels?path_png=f"F:\\kaggleDataSet\\diabeticRetinopathy\\rescaled_train_896\\18017_left.png" # detail
# This Python 3 environment comes with many helpful analytics libraries installed# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python# For example, here's several helpful packages to load in import numpy as np #
import numpy as npimport matplotlib.pyplot as pltx = np.random.randint(0,20,10)y = np.random.randint(0,20,10)print(x)print(y)plt.title("散点图")plt.scatter(x,y,edgecolors="red")plt.plot(x,y)plt.show() 解决绘图中文乱码,打开python的安装路径,找到“E:\pyth
import osimport numpy as npimport matplotlib.pyplot as pltfrom PIL import Image, ImageChopsfrom skimage import color,data,transform,io#获取所有数据文件夹名称fileList = os.listdir("F:\\data\\flowers")trainDataList = []trianLabel = []testDataList = []tes
par(ask=TRUE)opar <- par(no.readonly=TRUE) # save original parameter settingslibrary(vcd)counts <- table(Arthritis$Improved)counts # Listing 6.1 - Simple bar plot # vertical barplot barplot(counts, main="Simple Bar Plot",
par(ask=TRUE)opar <- par(no.readonly=TRUE) # make a copy of current settingsattach(mtcars) # be sure to execute this lineplot(wt, mpg)abline(lm(mpg~wt))title("Regression of MPG on Weight") # Input data for drug exampledose <- c(20, 30,
运行的效果如下:
................................................... (13)按格式输出 for i in range(0,len(List_row)): if (i%2==0): st=List_row[i].strip().split(' ') print(int(len(st)/2),file=dt) for j in range(0,len
import numpy as npfrom sklearn import datasets,linear_modelfrom sklearn.model_selection import train_test_splitdef load_data(): diabetes = datasets.load_diabetes() return train_test_split(diabetes.data,diabetes.target,test_size=0.25,random_state=0)#
import numpy as npimport matplotlib.pyplot as pltfrom sklearn import datasets, linear_modelfrom sklearn.model_selection import train_test_splitdef load_data(): diabetes = datasets.load_diabetes() return train_test_split(diabetes.data,diabetes.target
import numpy as npimport matplotlib.pyplot as pltfrom sklearn import datasets, linear_modelfrom sklearn.model_selection import train_test_splitdef load_data(): diabetes = datasets.load_diabetes() return train_test_split(diabetes.data,diabetes.target
运行的条件是一元逻辑向量(TRUE或FALSE)并且不能有缺失(NA)。else部分是可选的。如果 13 仅有一个语句,花括号也是可以省略的。下面的代码片段是一个例子:if(interactive()){ 14 plot(x, y) } else { png("myplot.png") plot(x, y) dev.off() 15 } 如果代码交互运
#----------------------------------------------## R in Action (2nd ed): Chapter 13 ## Generalized linear models ## requires packages AER, robust, gcc ## install.packages(c("AER", "robust", "
#-------------------------------------------------------------------## R in Action (2nd ed): Chapter 9 ## Analysis of variance ## requires packages multcomp, g
#---------------------------------------------------------------------## R in Action (2nd ed): Chapter 7 ## Basic statistics ## requires packages npmc, ggm
#---------------------------------------------------------------------## R in Action (2nd ed): Chapter 7 ## Basic statistics ## requires packages npmc, ggm,
#---------------------------------------------------------------## R in Action (2nd ed): Chapter 6 ## Basic graphs ## requires packages vcd, plotrix, sm, vioplot to be ins
#---------------------------------------------------------------------## R in Action (2nd ed): Chapter 7 ## Basic statistics ## requires packages npmc, ggm,
#---------------------------------------------------------## R in Action (2nd ed): Chapter 4 ## Basic data management ## requires that the reshape2 and sqldf packages have ## been instal
1.3 项目计划 第一周:深入学习和了解神经网络的工作原理,学习卷积的相关理论。 第二周:使用python的TensorFlow库,编写神经网络深度学习代码,搭建神经网络层,并且了解其工作原理和相关的计算、相关参数的传递等,到htttps://www.kaggle.com/moltean/fruits下载fruits压缩包,对数据进行初步的