pos = find(data.Type == 1); neg = find(data.Type == 0);
时间: 2023-07-15 11:13:49 浏览: 37
这段代码的作用是找到数据集中 Type 列中值为 1 的行所对应的索引和值为 0 的行所对应的索引,并将它们分别存储在 pos 和 neg 两个变量中。在这里,假设 data 是一个数据框(DataFrame)或者表格(Table),Type 是其中一列。find() 函数返回符合条件的行所对应的索引。
相关问题
要一个基于粗糙集理论能实现属性约简 算法的简单matlab代码
以下是一个基于粗糙集理论实现属性约简算法的简单 Matlab 代码示例:
```matlab
% 输入数据,假设有5个属性,每个属性有10个取值
data = randi([0 1], 10, 5);
% 标记属性是否被约简,初始时所有属性都未被约简
reduced = zeros(1, 5);
while any(reduced==0)
% 求出每个属性的重要度
importance = zeros(1, 5);
for i = 1:5
if reduced(i) == 0
% 计算属性 i 对分类的影响
pos = find(data(:,i)==1);
neg = find(data(:,i)==0);
pos_class = sum(data(pos, end)==1);
neg_class = sum(data(neg, end)==1);
pos_ratio = pos_class / length(pos);
neg_ratio = neg_class / length(neg);
if pos_ratio == 0 || neg_ratio == 0
importance(i) = Inf;
else
importance(i) = abs(pos_ratio - neg_ratio);
end
end
end
% 找到当前最重要的属性
[~, idx] = max(importance);
% 约简该属性
reduced(idx) = 1;
% 更新数据
data(:, idx) = [];
end
% 输出最终约简后的属性
reduced_attrs = find(reduced==1);
disp(['Reduced attributes: ' num2str(reduced_attrs)]);
```
该示例中,我们假设有5个属性,每个属性有10个取值。首先,我们标记所有属性都未被约简。然后,我们进入一个循环。
在每个循环中,我们计算每个属性对分类的影响,并找到当前最重要的属性。然后,我们将该属性约简,即从数据中删除该属性。我们更新标记数组,并继续下一个循环,直到所有属性都被约简。
最后,我们输出最终约简后的属性。在该示例中,我们输出所有被约简的属性的索引。
logistic回归代码matlab
Sure! Here's an example of logistic regression code in MATLAB:
```matlab
% Load the dataset
data = load('dataset.mat');
X = data.X;
y = data.y;
% Feature scaling
X = (X - mean(X)) ./ std(X);
% Add intercept term
X = [ones(size(X, 1), 1), X];
% Initialize parameters
initial_theta = zeros(size(X, 2), 1);
% Set options for fminunc optimization function
options = optimset('GradObj', 'on', 'MaxIter', 400);
% Define the cost function and gradient
costFunction = @(theta) logisticCostFunction(theta, X, y);
[theta, ~] = fminunc(costFunction, initial_theta, options);
% Predict using the learned parameters
probabilities = sigmoid(X * theta);
predictions = probabilities >= 0.5;
% Plot the decision boundary
plotDecisionBoundary(theta, X, y);
% Define the sigmoid function
function g = sigmoid(z)
g = 1 ./ (1 + exp(-z));
end
% Define the cost function and its gradient
function [cost, gradient] = logisticCostFunction(theta, X, y)
m = length(y);
h = sigmoid(X * theta);
cost = (-1 / m) * sum(y .* log(h) + (1 - y) .* log(1 - h));
gradient = (1 / m) * (X' * (h - y));
end
% Define the function to plot the decision boundary
function plotDecisionBoundary(theta, X, y)
plotData(X(:,2:end), y);
hold on;
if size(X, 2) <= 3
plot_x = [min(X(:,2))-2, max(X(:,2))+2];
plot_y = (-1 ./ theta(3)) .* (theta(2) .* plot_x + theta(1));
plot(plot_x, plot_y, '-g', 'LineWidth', 2);
else
u = linspace(-1, 1.5, 50);
v = linspace(-1, 1.5, 50);
z = zeros(length(u), length(v));
for i = 1:length(u)
for j = 1:length(v)
z(i,j) = mapFeature(u(i), v(j)) * theta;
end
end
z = z';
contour(u, v, z, [0, 0], 'LineWidth', 2);
end
hold off;
end
% Define the function to plot the data points
function plotData(X, y)
pos = find(y == 1);
neg = find(y == 0);
scatter(X(pos, 1), X(pos, 2), 'k+', 'LineWidth', 2);
hold on;
scatter(X(neg, 1), X(neg, 2), 'ko', 'MarkerFaceColor', 'y');
hold off;
end
% Define the function to map features for more complex decision boundaries
function out = mapFeature(X1, X2)
degree = 6;
out = ones(size(X1(:,1)));
for i = 1:degree
for j = 0:i
out(:, end + 1) = (X1.^(i-j)) .* (X2.^j);
end
end
end
```
Please make sure to replace `'dataset.mat'` with the actual filename of your dataset.