if __name__ == '__main__': df = getIrisData() trainEx(df,[NAMES_FLOWERS[0],NAMES_FLOWERS[1]]) 结果 补充定义: #data column names LENGTH_SEPAL = 'sepalLength' WIDTH_SEPAL = 'sepalWidth' LENGTH_PETAL = 'petalLength' WIDTH_PETAL = 'petalWidth' LABEL_NAME = 'flowerName' #flower...
x后缀为特征值,y后缀为标签 #下载数据集,定义常量TRAIN_URL="http://download.tensorflow.org/data/iris_training.csv"TEST_URL="http://download.tensorflow.org/data/iris_test.csv"CSV_COLUMN_NAMES=['SepalLength','SepalWidth','PetalLength','PetalWidth','Species']SPECIES=['Setosa','Versicolor','Vir...
SRIN: A New Dataset for Social Robot Indoor Navigation A Participatory Model for the Regeneration of Australian Cities: The Case of Geelong Internet + and Transportation System The Analysis of Spalling Effect on the Stress Distribution of Underground Pillar Spalling Using FLAC3D Developing Virtual Equip...
INSERT INTO iris_data ("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species", "SpeciesId") EXEC dbo.get_iris_dataset; If you're new to T-SQL, be aware that the INSERT statement only adds new data; it won't check for existing data, or delete and rebuild th...
(train_path,names=FUTURES,header=0)train_x,train_y=train,train.pop('Species')test=pd.read_csv(test_path,names=FUTURES,header=0)test_x,test_y=test,test.pop('Species')feature_columns=[]forkeyintrain_x.keys():feature_columns.append(tf.feature_column.numeric_column(key=key))print(test_x...
Each ith column of the input matrix will have four elements representing the four measurements taken on a single flower. Here such a dataset is loaded. x = iris_dataset; We can view the size of inputsX. Note thatXhas 150 columns. These represent 150 sets of iris flower attributes. It ...
Example (return all keys and names from the 'Person' global): query = db.mglobalquery({global: "Person", key: [""]}, {multilevel: false, getdata: true}); Traversing the dataset In key order: result = query.next(); In reverse key order: result = query.previous(); In all ...
If a Select sequence of dictionary's is the last func_adl expression, then a file called xaod_output.root will be generated, and it will contain a TTree called atlas_xaod_tree, with column names taken from the dictionary keys. ServiceX (and the servicex frontend package) can convert fr...
cf.fit(data,t) # training on the iris dataset print (cf.predict(data[0])) #训练完分类1条数据 #output:[1.] print (t[0]) #output:1.0#从原始数据data中划分为训练集和验证集,t也做同样划分fromsklearn import cross_validation train, test, t_train, t_test=cross_validation.train_test_split...
Remember, in the properties of the Raster Functions pane, you had specified in the Name field where the dataset type was to be found. Now, here you have to enter the values in this field that are to be used: In the Name column for the parameter Raster, specify DSM. ...