kaggle datasets

bx, by = Cells_per_block sx, sy = image.shape n_cellsx = Int. (Np.floor (SX/CX)) # Number of cells in x N _cellsy = Int (Np.floor (sy/CY)) # Number of cells in y N_blocksx = (N_CELLSX-BX) + 1 N_blocksy = (n_cellsy-by ) + 1 GX = zeros (SX, SY), dtype=np.double) Gy = zeros (SX, SY), dtype=np.double) EPS = 1e-5 grad = zeros (SX, Sy, 2), dtype=np.double) for I in xrange (1, sx-1): for J in Xrange (1, sy-1): Gx[i, j] = Image[i, j-1 ]-Image[i, j+1] gy[i, j] = Image[i+1, j]-Image[i-1, J] Grad[i, j, 0

(); } PrivateDataTable Createdatatable () {DataTable dt=NewDataTable (); Dt. Columns.Add ("ProductCode",typeof(string)); Dt. Columns.Add ("saledate",typeof(DateTime)); Dt. Columns.Add ("SaleAmount",typeof(Double)); DataRow Dr=dt. NewRow (); dr["ProductCode"] ="0001"; dr["saledate"] = Convert.todatetime ("2009-2-1"); dr["SaleAmount"] = +; Dt. Rows.Add (DR); Dr=dt. NewRow (); dr["ProductCode"] ="0001"; dr["saledate"] = Convert.todatetime ("2009-1-1"); dr["SaleAmount"] = -;

2.2.2 MatrixMatrix (Vector,nrow,ncol,byrow,dimnames,char_vector_rownames,char_vector_colnames)whichByrow=true/false, which is filled by row or column, is populated by column by default2.2.4 Data Frame1.attach,detach () and with ()Attach (): Add data frame to search pathDetach (): Remove the data frame from the search pathWith (): assignment is only valid in parentheses, if you want to take effect outside of parentheses, use 2.2.5 FactorFactor: A nominal variable and an ordered variable that corr

New FMEOFeature object is created before the read method is called each time. To avoid overwriting the elements of the previous read. Restrictions By executing simple space query and attribute query, the FME object is restricted to read only matching elements. After reading the last element, your application can use the setConstraints method to filter new elements. Use the setConstraints method to specify constraints for the FMEOFeature object and use the attribute to specify fme_search_type f

In practical applications, we often convert wide data (one patient observation) into long data (one patient observation) or long data (one patient multiple observations) into wide data (one observation for a patient), and in R we can use the Reshape2 package. There are two implementations of the SAS: arrays and transpose. This blog post first explains the use of arrays to reconstruct SAS data, and the next blog post will introduce the use of the transpose function to reconstruct SAS data.1. Wide

any structure mentioned so far.Supplemental Attach (), detach (), and with ()Take the Mtcars dataset in R as an examplefunction attach () to add a data frame to the search path of R. R after encountering a variable name, the data frame in the search path is checked to locate the variable.The function detach () removes the data frame from the search path. It is important to note that detach () does not do any processing on the data frame itself.In addition, the other way is to use the function w

=train_model (train_x,train_y,classnum)For I in Range (Classnum):Print (Prior_probability[i]) #输出一下每个标签的总共数量Time3=time.time ()Print ("Train data Cost", Time3-time2, "second")Print ("Start predicting data ...")Predict_y=predict (test_x,test_y,prior_probability,conditional_probability)Time4=time.time ()Print ("Predict data Cost", Time4-time3, "second")Print ("Start calculate accuracy ...")Acc=cal_accuracy (test_y,predict_y)Time5=time.time ()Print ("Accuarcy", ACC)Print ("Calculate Accuarcy cost",

properties:Perimeter PerimeterCompactness CompactLength of kernel coresWidth of kernel core widthAsymmetry coefficient asymmetry coefficientLength of kernel groove grain lengthInput: These attributes aboveOutput: It's the kind of discrimination that belongs.5. "Does the Indians have diabetes"?(Pima Indians Diabetes Data Set) is determined by studying the properties of eight numeric types and then by the corresponding conclusions.The last part of the dataset is a categorized attribute: 0 means n

Detection_output_layer.cu,detection_output_layer.cpp two files, and then Detection_output_ LAYER.HPP statement # include //#include "thrust/functional.h " //#include " Thrust/sort.h ".....Thrust::sort_by_key (confidence[0],confidence[0]+num_remain,idx[0],Thrurst::greater5 After the above execution, congratulations you can basically generate Libcaffe, caffe.exe files, first compile Libcaffe, and then Caffe (usually release under).6 after the simple, write a bat command, set up the corresponding

server| binary uses datasets to access binaries in SQL Server Author Zhu Yi The dataset makes it easy to access and update binary files in SQL Server, and the following is a detailed code demonstration Demo Environment: Database machine Name: s_test Login name: SA Password: 7890 Database name Db_test Set up a table below: CREATE TABLE tb_test (ID int identity (1,1), photo image, constraint pk_tb_test key (ID)) First, save the files on the har

Overview The origin of relational database originates from the concept of set in mathematics. Therefore, between the collection and the collection, the operation between the mathematical sets is also inherited. For relational databases, relationships are often used in relational databases that are not directly related to two datasets. For example, the foreign key. But two data rallies have indirect relationships, such as two games, where there is an

Catalog is a dataset used to index other datasets on z/OS, and many times the operation of accessing the dataset on the system is catalog, so increasing the performance of each catalog on the system can directly improve the performance of the Access DataSet. Catalog uses caching's structure to cache some catalog records that often have read and update requests, thereby shortening the time to access these catalog and reaching a system-level performance

a resolution of 384x286 pixels. Each one shows the frontal view of a face of one out of different test persons.TopMIT cbcl face Data Set:Available at: http://www.ai.mit.edu/projects/cbcl/software-datasets/FaceData2.html target=A training set consists of 6,977 cropped images (2,429 faces and 4,548 nonfaces), and the test set consists of 24,045 imag ES (472 faces and 23,573 nonfaces).TopFERET Database:Available at: http://www.nist.gov/srd/This database

and All keywords2.1:except (default column corresponds to location action)By default, this process is carried out in two steps1: Make a unique, delete the duplicate rows in one.2: Delete One of the rows from one to the other by comparing one to the other.Add the ALL keyword separatelyDo not make a unique step and keep filtering as it is. (Omit the first step to improve efficiency)Add Corr keyword separatelyMerge by column name, and delete all column names.Perform a unique step, and then delete

/train_lmdb $DATA/imagenet_mean.binaryproto– Be careful here,$EXAMPLE/caffe/examples/lmdb_test/train/train_lmdb The example here is needed for your training set Lmdb path$DATA represents the directory to generate the mean file, and the file name you can easily modify, storage path can be arbitrary.Then run as before.2, Mean.binaryproto turn mean.npyWhen working with the C + + interface of Caffe, the required image mean file is PB format, for example, the common mean file name is Mean.binarypro

the + while(Myread. Read)'not empty then read all the time A theMyread. GetValues (MYSTR)'performs a read action, storing a row of data in the MYSTR array +MyTable. Rows.Add (MYSTR)'add array data as a row to the table - End while $ $ 'bind a table to a real-world control -Datagridview1.datamember ="mytable" -Datagridview1.datasource =mytable the -Myread. Close ()'Turn off Read WuyiMyconnect. Close ()'Close Connection the - End Sub Wu - About

Usage scenarios:For example, you need to query all student numbers with scores greater than 95, separated by commas into a string, from the Student score table.To prepare the test data:CREATE TABLE score (ID int,score int)INSERT into score values (1,90)INSERT into score values (2,96)INSERT into score values (3,99)It is now necessary to query the result string "2,,3" with a single statement.The SQL Server statements are as follows:Select substring ((SELECT ', ' +cast (id as varchar) from score wh

': [2008, 2014]}) display (' Df1 ', ' Df2 ') Using the Pandas Library's merge function can help us to merge data, and we can see that in the merged data frame DF3 includes the employee's corresponding group and employment date information: DF3 = Pd.merge (df1, DF2) df3 Similarly, we can use this function to incorporate more information, such as the supervisory leadership of each employee: DF4 = PD. Dataframe ({' Group ': [' Accounting ', ' Engineering ', ' HR '], ' super

Matlab codes and datasets for Feature Learning dimensionality reduction (subspace Learning)/Feature selection/topic mo Deling/matrix factorization/sparse coding/hashing/clustering/active Learning We provide here some matlab codes o F feature learning algorithms, as as and some datasets in MATLAB format. All this codes and data sets are used in our experiments. The processed data in MATLAB format can is used

stage2_fast_rcnn_train.pt stage2_rpn_train.pt faster_rcnn_test.pt Second, modify the parameters of the processing data section for a specific data set, including: Lib/datasets below: pascal_voc.py Lib/datasets below: imdb.py At last, the training parameters are modified for the training process: (Batch_size--lib faster rcnn in the config.py of change, learning rate;--Modify in model; max_iters--in Tools)