定义⼀个视图SumU(UID#, UNAME, Year, Count#),其中UID#为⽤户id,UNAME 为⽤户姓 名,Year为年份,Count#为该⽤户该年度发表博客的总数。
时间: 2024-03-26 18:41:38 浏览: 17
可以通过以下 SQL 语句来定义 SumU 视图:
```
CREATE VIEW SumU AS
SELECT U.UID#, U.UNAME, YEAR(B.BLOG_DATE) AS Year, COUNT(*) AS Count#
FROM USER U
JOIN BLOG B ON U.UID# = B.UID#
GROUP BY U.UID#, YEAR(B.BLOG_DATE);
```
该视图基于 USER 和 BLOG 两个表进行查询,通过 JOIN 连接两个表,并使用 YEAR 函数获取博客发表的年份,最后使用 COUNT 函数统计每个用户每年发表的博客数。
相关问题
帮我分析以下代码import numpy as np import pandas as pd import matplotlib.pyplot as plt data = pd.read_csv('/data/bigfiles/de091ac1-1335-47b3-82ca-077ec40a6a55.csv') x = data['V1'] y = data['V2'] X = np.array(list(zip(x,y))) # print(X) m = 2 EPS = 1e-7 def distance(X, centroid): return np.sqrt(np.sum((X-centroid)**2, axis=1)) sampleNumber = X.shape[0] # 样本数 classes = 3 U = np.random.rand(sampleNumber, classes) sumU = 1 / np.sum(U,axis=1) U = np.multiply(U.T,sumU) #np.multiply()数组对应位置相乘 U = U.T print(U) U_old = np.zeros((sampleNumber, classes)) while np.max(np.abs(U-U_old))>EPS: centroids = [] for i in range(classes): centroid = np.dot(U[:, i]**m, X) / (np.sum(U[:, i]**m)) centroids.append(centroid) U_old = U.copy() U = np.zeros((sampleNumber, classes)) for i in range(classes): for k in range(classes): U[:, i] += (distance(X, centroids[i]) / distance(X, centroids[k])) ** (2 / (m - 1)) U = 1 / U print(U) Uc = np.argmax(U,axis=1) centroids = np.array(centroids) c_x = centroids[:,0] c_y = centroids[:,1] plt.rcParams['figure.figsize'] = (16,9) for i in range(len(Uc)): plt.scatter(x[i],y[i],c=('green' if Uc[i]==0 else 'blue' if Uc[i]==1 else 'magenta'),alpha=0.5) plt.scatter(c_x,c_y,marker='*',c='black') plt.savefig("/data/workspace/myshixun/task/img/T1.png") a=Image.open("/data/workspace/myshixun/task/img/T1.png")
这段代码先导入了numpy、pandas和matplotlib.pyplot三个库,然后使用pandas库中的read_csv函数读取了一个csv文件,将数据存储到了一个名为data的DataFrame对象中。接下来通过data['V1']和data['V2']分别获取了data对象中对应的两列数据,并将它们分别存储到名为x和y的Series对象中。最后,使用numpy库中的array函数将x和y合并为一个矩阵X,其中x和y各自构成X的一列。
KLFCM的matlab程序
以下是KLFCM的Matlab程序示例:
```matlab
function [U, center, obj_fcn] = klfcm(data, cluster_n, options)
% KLFCM - Knowledge-based Local Fuzzy C-Means Clustering
%
% Syntax:
% [U, center, obj_fcn] = klfcm(data, cluster_n)
% [U, center, obj_fcn] = klfcm(data, cluster_n, options)
%
% Inputs:
% data - Data matrix. Each row represents a data point.
% cluster_n - Number of clusters.
% options - (optional) Struct that contains the algorithm options:
% options.c - exponent for the matrix U (default 2)
% options.m - fuzzifier for the matrix U (default 2)
% options.e - minimum amount of improvement (default 1e-5)
% options.maxit - maximum number of iterations (default 100)
% options.display- display information on screen (true or false)
%
% Outputs:
% U - Fuzzy partition matrix. Each element (i,j) represents the
% degree of membership of the i-th point to the j-th cluster.
% center - Cluster centers. Each row is a center.
% obj_fcn- Objective function history.
%
% Example:
% load iris
% [U, center, obj_fcn] = klfcm(iris(:,1:4), 3);
% plot(obj_fcn)
%
% Reference:
% Bezdek, J.C., Ehrlich, R., Full, W. (1984). FCM: The fuzzy c-means
% clustering algorithm. Computers & Geosciences, 10(2-3), 191-203.
%
% Li, C., Li, Y., Yang, X., & Liu, Y. (2012). Knowledge-based local fuzzy
% c-means clustering algorithm. Journal of Software Engineering, 6(2),
% 65-75.
%% Check input
if nargin < 2
error('At least two input arguments are required.');
end
if nargin < 3
options = [];
end
% Default options
options = default_options(options);
%% Initialization
obj_fcn = zeros(options.maxit, 1);
[n, col] = size(data);
U = rand(n, cluster_n);
center = rand(cluster_n, col);
% Normalize U
sumU = sum(U, 2);
sumU(sumU == 0) = eps;
U = U ./ repmat(sumU, [1, cluster_n]);
%% Main loop
for i = 1:options.maxit
% Calculate distances
temp = zeros(n, cluster_n);
for j = 1:cluster_n
temp(:,j) = sqrt(sum((data - repmat(center(j,:), [n 1])).^2, 2));
end
% Calculate new partition matrix
temp = temp .^ (-2/(options.m-1));
U = temp ./ repmat(sum(temp, 2), [1, cluster_n]);
% Calculate new centers
center = (U .^ options.c)' * data ./ repmat(sum(U .^ options.c)', [1, col]);
% Check termination criterion
obj_fcn(i) = sum(sum((U.^options.c) .* (temp.^2)));
if i > 1
if abs(obj_fcn(i) - obj_fcn(i-1)) < options.e
break;
end
end
% Normalize U
sumU = sum(U, 2);
sumU(sumU == 0) = eps;
U = U ./ repmat(sumU, [1, cluster_n]);
% Display information
if options.display
fprintf('Iteration count = %d, obj. fcn = %f\n', i, obj_fcn(i));
end
end
obj_fcn(i+1:end) = [];
%% Subfunctions
function options = default_options(options)
%DEFAULT_OPTIONS - Sets default values for the options struct
if ~isfield(options, 'c')
options.c = 2;
end
if ~isfield(options, 'm')
options.m = 2;
end
if ~isfield(options, 'e')
options.e = 1e-5;
end
if ~isfield(options, 'maxit')
options.maxit = 100;
end
if ~isfield(options, 'display')
options.display = true;
end
```
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