iris biometrics

Method 1: Open Excel firstData in the above column -- import external data -- "file type" ("all data") in the dialog box ")Change to "all files" -- select your ***. data File -- open -- next -- select "comma" in "separator" -- "next --" complete -- "create worksheet --" OK -- "save the file and do not forget in English !!!!Drag the Excel file to the workspace in MATLAB. Method 2: Read data from the UCI dataset Iris. Data: > [Attrib1, attrib2, attrib3

Calculates the maximum, average, median, and mean variance of the iris petal length.Generates a random array of normal distributions with np.random.normal () and displays them.NP.RANDOM.RANDN () produces a random array of normal distributions and displays them.Shows the normal distribution of iris petal length, graph, scatter plot.Code:ImportNumPy as NP fromSklearn.datasetsImportLoad_irisImportMatplotlib.py

Install package: > install.packages("e1071") Import e1071: > library(e1071) Find a dataset: > data(iris)> iris Sepal.Length Sepal.Width Petal.Length Petal.Width Species1 5.1 3.5 1.4 0.2 setosa2 4.9 3.0 1.4 0.2 setosa3 4.7 3.2 1.3 0.2 setosa4 4.6 3.1

In R, you can use the various functions provided by the e1071 package to perform data analysis and mining tasks based on support vector machines. Please install and correctly reference the e1071 package before using the related function. One of the most important functions in this package is the SVM () function used to build the support vector machine model. We will use the following example to demonstrate its usage.The data in the following example is derived from an important paper published b

system", The diagram is called the CIE 1931 chromaticity chart. In 1964, the results of the study, published in 1959 by Stiles (W.s Stiles) and Birch (J.m.bruch) and Sprinskaya (N.i.speranskaya), resulted in the development of CIE1964 complementary chromaticity systems and corresponding chromaticity maps, It is widely used in various countries in the world for chromaticity calculation and chromatic aberration calculation. In 1964, a three-dimensional concept of "uniform color space" was propose

1. Pandas PlottingImportMatplotlib.pyplot as PltImportNumPy as NPImportPandas as PD%matplotlib Notebookplt.style.use ("Seaborn-colorblind") Np.random.seed (123)#Cumsum:add value_of_i + value_of_i+1 = value_of_i+2DF = PD. DataFrame ({'A': Np.random.randn (365). Cumsum (0),'B': Np.random.randn (365). Cumsum (0) + 20, 'C': Np.random.randn (365). Cumsum (0)-20}, Index=pd.date_range ('1/1/2017', periods=365))#Create a scatter plot of columns ' a ' and ' C ', with changing color (c)

Today, when reading Professor Wu Xizhi's "Complex data statistics method", encountered a data set according to a certain factor into subsets, and then a few subsets randomly divided into n parts of the problem, Professor Wu's method is better understood, but I still feel a bit cumbersome, so I wrote a function, After that, you just need to run the function.This uses the iris dataset that comes with R,> str (IRIS