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1. KNN principle: There is a collection of sample data, also called a training sample set, and there is a label for each data in the sample set, that is, we know the correspondence between each data in the sample set and the owning category. After entering new data with no labels, each feature of the new data is compared with the characteristics of the data in the sample set, and the algorithm extracts the category labels of the most similar data (nearest neighbor) in the sample set. In general,
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
recognition. Figure 02 After you click OK, PS will automatically select similar pixel fills based on the surrounding selection and background. The effect is shown in the following illustration. Figure 03 Apply the same method to dispose of the tape, after processing, press Ctrl+d to cancel the selection. Figure 04 Step 3 The dog's nose is a bit dirty in the picture, and the stains and spots are distracting, so it's also going to be erased. The Healing Brush tool is used here.
lower the F value, the greater the luminous flux. The law of the F-value is that the last value is exactly twice times the previous one, so the aperture is twice times less than the number of light. Common values are 1.4, 2, 2.8, 4, 5.6, 8, 11, 16, 22, and several others.The general aperture can be adjusted to have a manual aperture (manual iris) and an automatic aperture (autoiris).Manual aperture Industrial lens is the simplest industrial lens, sui
two kinds and the population obeys multivariate normal distribution. code example: NBSP; > if (Require (MASS) == FALSE) + " mass " ) +} > > Model1=lda (Species~.,data=iris) > table class ) > table Setosa versicolor virginica setosa 50 0 0 versicolor 0 2 virginica 0 1 49> sum (diag (prop.table (table))) ## #判对率 [1] 0.98 as a result, only three of the samples were observed to be judged incorrectly. After the discriminant function is est
The process of K-means clustering is demonstrated below on the iris dataset.First remove the species property from the iris dataset, then call the function Kmeans on the dataset Iris and store the cluster results in a variable kmeans.result.In the following code, the number of clusters is set to 3.Iris2 Iris2$species (Kmeans.result Compare cluster results to clas
Preface Based on official documentation, the iris dataset is mainly categorized. Use Tf.contrib.learn.tf.contrib.learn to quickly build a deep network classifier, step into CSV data set up network classifier Training Network compute the correct rate of the test sets and classify the new samples The iris DataSet contains 150 rows of data and has three different types of
it in the face so that it touches the tip of the nose. Using the nose as a key object, align the ellipse to the nose. To do this, you need to select 2 shapes and hold down the ALT key when you click the nose shape. You will see a thick stroke around your nose to indicate the key object. Go to the Alignment panel and click the Horizontal center alignment. Then select the Anchor Point tool (SHIFT-C) and move down the anchor handle of the bottom anchor point to about 45 °. Use a
Often thought to climb the mountains small, can, often and actually come to the beginning, Daniel, slowly step to my notes PA share it, please~ ——————————————————————————— Follow-up add more content: Application One: What are the members of the If family? --if/ifelse/stopifnot Application Two: How to consume real time output in the loop. ———————————————————————————————————— 1. Circulation # #循环for Iris allzl=unique (
are normally distributed (for example,), they are mainly used for numeric features. Use the data in the scikit-learn package. The code and description are as follows: >>> From sklearn import datasets # import data in the package >>> iris = datasets. load_iris () # load data> iris. feature_names # display Feature Names ['sepal length (cm) ', 'sepal width (cm)', 'petal length (cm) ', 'petal width (cm) '] >>
://detectmobilebrowsers.com/ As Nginx is used in this article, you only need to download the Nginx configuration on the website. Set $ mobile_rewrite do_not_perform; if ($ http_user_agent ~ * "(Android | bb \ d + | meego ). + mobile | avantgo | bada \/| blackberry | blazer | compal | Eline | fennec | hiptop | iemobile | ip (hone | od) | iris | kindle | lge | maemo | midp | mmp | mobile. + firefox | netfront | opera m (ob | in) I | palm (OS )? | Phone
do_not_perform;if ($http _user_agent ~* "(android|bb\d+|meego). +mobile|avantgo|bada\/|blackberry |blazer|compal|elaine|fennec|hiptop|iemobile|ip (hone|od) |iris|kindle|lge |maemo|midp|mmp|mobile.+firefox| Netfront|opera m (ob|in) i|palm (OS)? | Phone|p (Ixi|re) \/|plucker|pocket|psp|series (4|6) 0|symbian|treo|up\. (browser|link) |vodafone|wap|windows Ce|xda|xiino ") {set $mobile _rewrite perform;} if ($http _user_agent ~* "^ (1207|6310|6590|3gso|4t
Apache rewrite check mobile device Access website The code is as follows Copy Code Rewriteengine onRewritebase/ Rewritecond%{http_user_agent} (Android|bbd+|meego). +mobile|avantgo|bada/|blackberry|blazer|compal|elaine|fennec |hiptop|iemobile|ip (hone|od) |iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m (ob|in) i|palm (OS)? | Phone|p (Ixi|re)/|plucker|pocket|psp|series (4|6) 0|symbian|treo|up. (Browser|link
versicolor types of iris, but the other two are wrongly predicted as virginica types; The model correctly predicts 48 of the irises that belong to the Virginica type, but also predicts the other two incorrectly as the versicolor type.An optional parameter in the function predict () is decision.values, and we also discuss the use of this parameter briefly here. By default, the default value for this parameter is false. If it is set to true, then the r
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