oracle的练习表---employee

转载于:http://lj6684.iteye.com/blog/771681

 

Oracle数据库安装的时候会自带一个练习用数据库(其中包含employee表,后来版本中此表改名为emp); 

首先在安装过程中应该有个选项“是否安装实例表”(完全安装模式下默认是选择的),需要选择才有此表; 

此表归属于scott账户,scott用户默认口令为tiger 
如果发现scott账户已过期(Oracle 10g中经常发生),或口令不正确,可以通过以下方法进行设置 :

 

 

--账户解锁
alter user scott account unlock;
--重设scott账户口令为tiger
alter user scott identified by tiger;

 适用于Oracle的SQL:

 

----------------------demobld.sql begin-----------------------
--
-- Copyright (c) Oracle Corporation 1988, 2000. All Rights Reserved.
--
-- NAME
-- demobld.sql
--
-- DESCRIPTION
-- This script. creates the SQL*Plus demonstration tables in the
-- current schema. It should be STARTed by each user wishing to
-- access the tables. To remove the tables use the demodrop.sql
-- script.
--
-- USAGE
-- From within SQL*Plus, enter:
-- START demobld.sql
SET TERMOUT ON
PROMPT Building demonstration tables. Please wait.

SET TERMOUT OFF
DROP TABLE EMP;
DROP TABLE DEPT;
DROP TABLE BONUS;
DROP TABLE SALGRADE;
DROP TABLE DUMMY;

CREATE TABLE EMP
(EMPNO NUMBER(4) NOT NULL,
ENAME VARCHAR2(10),
JOB VARCHAR2(9),
MGR NUMBER(4),
HIREDATE DATE,
SAL NUMBER(7, 2),
COMM NUMBER(7, 2),
DEPTNO NUMBER(2));
INSERT INTO EMP VALUES
(7369, 'SMITH', 'CLERK', 7902,
TO_DATE('17-DEC-1980', 'DD-MON-YYYY'), 800, NULL, 20);
INSERT INTO EMP VALUES
(7499, 'ALLEN', 'SALESMAN', 7698,
TO_DATE('20-FEB-1981', 'DD-MON-YYYY'), 1600, 300, 30);
INSERT INTO EMP VALUES
(7521, 'WARD', 'SALESMAN', 7698,
TO_DATE('22-FEB-1981', 'DD-MON-YYYY'), 1250, 500, 30);
INSERT INTO EMP VALUES
(7566, 'JONES', 'MANAGER', 7839,
TO_DATE('2-APR-1981', 'DD-MON-YYYY'), 2975, NULL, 20);

INSERT INTO EMP VALUES
(7654, 'MARTIN', 'SALESMAN', 7698,
TO_DATE('28-SEP-1981', 'DD-MON-YYYY'), 1250, 1400, 30);

INSERT INTO EMP VALUES(7698, 'BLAKE', 'MANAGER', 7839,
TO_DATE('1-MAY-1981', 'DD-MON-YYYY'), 2850, NULL, 30);

INSERT INTO EMP VALUES
(7782, 'CLARK', 'MANAGER', 7839,
TO_DATE('9-JUN-1981', 'DD-MON-YYYY'), 2450, NULL, 10);

INSERT INTO EMP VALUES
(7788, 'SCOTT', 'ANALYST', 7566,
TO_DATE('09-DEC-1982', 'DD-MON-YYYY'), 3000, NULL, 20);

INSERT INTO EMP VALUES
(7839, 'KING', 'PRESIDENT', NULL,
TO_DATE('17-NOV-1981', 'DD-MON-YYYY'), 5000, NULL, 10);

INSERT INTO EMP VALUES
(7844, 'TURNER', 'SALESMAN', 7698,
TO_DATE('8-SEP-1981', 'DD-MON-YYYY'), 1500, 0, 30);

INSERT INTO EMP VALUES
(7876, 'ADAMS', 'CLERK', 7788,
TO_DATE('12-JAN-1983', 'DD-MON-YYYY'), 1100, NULL, 20);

INSERT INTO EMP VALUES
(7900, 'JAMES', 'CLERK', 7698,
TO_DATE('3-DEC-1981', 'DD-MON-YYYY'), 950, NULL, 30);

INSERT INTO EMP VALUES
(7902, 'FORD', 'ANALYST', 7566,
TO_DATE('3-DEC-1981', 'DD-MON-YYYY'), 3000, NULL, 20);

INSERT INTO EMP VALUES
(7934, 'MILLER', 'CLERK', 7782,
TO_DATE('23-JAN-1982', 'DD-MON-YYYY'), 1300, NULL, 10);

CREATE TABLE DEPT
(DEPTNO NUMBER(2),
DNAME VARCHAR2(14),
LOC VARCHAR2(13) );

INSERT INTO DEPT VALUES (10, 'ACCOUNTING', 'NEW YORK');
INSERT INTO DEPT VALUES (20, 'RESEARCH', 'DALLAS');
INSERT INTO DEPT VALUES (30, 'SALES', 'CHICAGO');
INSERT INTO DEPT VALUES (40, 'OPERATIONS', 'BOSTON');

CREATE TABLE BONUS
(ENAME VARCHAR2(10),
JOB VARCHAR2(9),
SAL NUMBER,
COMM NUMBER);
CREATE TABLE SALGRADE
(GRADE NUMBER,
LOSAL NUMBER,
HISAL NUMBER);

INSERT INTO SALGRADE VALUES (1, 700, 1200);
INSERT INTO SALGRADE VALUES (2, 1201, 1400);
INSERT INTO SALGRADE VALUES (3, 1401, 2000);
INSERT INTO SALGRADE VALUES (4, 2001, 3000);
INSERT INTO SALGRADE VALUES (5, 3001, 9999);

CREATE TABLE DUMMY
(DUMMY NUMBER);

INSERT INTO DUMMY VALUES (0);
COMMIT;
SET TERMOUT ON
PROMPT Demonstration table build is complete.
EXIT
-------------------demobld.sql end-----------------------

 

 

适用于MySQL的:

 

DROP TABLE employee;
DROP TABLE dept;
DROP TABLE bonus;
DROP TABLE salgrade;
DROP TABLE dummy;

CREATE TABLE employee(  
           employeeno DECIMAL(4) NOT NULL,
           ename VARCHAR(10),
           job VARCHAR(9),
           mgr DECIMAL(4),
           hiredate DATE,
           sal DECIMAL(7, 2),
           comm DECIMAL(7, 2),
          deptno DECIMAL(2)
);

INSERT INTO employee VALUES(7369, 'SMITH', 'CLERK', 7902, '1980-12-17', 800, NULL, 20);
INSERT INTO employee VALUES(7499, 'ALLEN', 'SALESMAN', 7698,'1981-2-20', 1600, 300, 30);
INSERT INTO employee VALUES(7521, 'WARD', 'SALESMAN', 7698, '1981-2-22', 1250, 500, 30);
INSERT INTO employee VALUES(7566, 'JONES', 'MANAGER', 7839, '1981-4-2', 2975, NULL, 20);
INSERT INTO employee VALUES(7654, 'MARTIN', 'SALESMAN', 7698, '1981-9-28', 1250, 1400, 30);
INSERT INTO employee VALUES(7698, 'BLAKE', 'MANAGER', 7839, '1981-5-1', 2850, NULL, 30);
INSERT INTO employee VALUES(7782, 'CLARK', 'MANAGER', 7839, '1981-7-9', 2450, NULL, 10);
INSERT INTO employee VALUES(7788, 'SCOTT', 'ANALYST', 7566, '1982-12-9', 3000, NULL, 20);
INSERT INTO employee VALUES(7839, 'KING', 'PRESIDENT', NULL, '1981-11-17', 5000, NULL, 10);
INSERT INTO employee VALUES(7844, 'TURNER', 'SALESMAN', 7698, '1981-9-8', 1500, 0, 30);
INSERT INTO employee VALUES(7876, 'ADAMS', 'CLERK', 7788, '1983-1-12', 1100, NULL, 20);
INSERT INTO employee VALUES(7900, 'JAMES', 'CLERK', 7698, '1981-12-3', 950, NULL, 30);
INSERT INTO employee VALUES(7902, 'FORD', 'ANALYST', 7566, '1981-12-3', 3000, NULL, 20);
INSERT INTO employee VALUES(7934, 'MILLER', 'CLERK', 7782, '1982-1-23', 1300, NULL, 10);

CREATE TABLE dept
(deptno DECIMAL(2),
dname VARCHAR(14),
loc VARCHAR(13) );

INSERT INTO dept VALUES (10, 'ACCOUNTING', 'NEW YORK');
INSERT INTO dept VALUES (20, 'RESEARCH', 'DALLAS');
INSERT INTO dept VALUES (30, 'SALES', 'CHICAGO');
INSERT INTO dept VALUES (40, 'OPERATIONS', 'BOSTON');

CREATE TABLE bonus
(ename VARCHAR(10),
job VARCHAR(9),
sal DECIMAL,
comm DECIMAL);

CREATE TABLE salgrade
(grade DECIMAL,
losal DECIMAL,
hisal DECIMAL);

INSERT INTO salgrade VALUES (1, 700, 1200);
INSERT INTO salgrade VALUES (2, 1201, 1400);
INSERT INTO salgrade VALUES (3, 1401, 2000);
INSERT INTO salgrade VALUES (4, 2001, 3000);
INSERT INTO salgrade VALUES (5, 3001, 9999);

CREATE TABLE dummy(
 dummy DECIMAL
);

INSERT INTO dummy VALUES (0);
MySQL 测试样例数据Employees MySQL 测试样例数据库 Employees, Employees Sample Database、datacharmer/test_db,airportdb Database 阅读详情

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Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

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