我的队友和我正在尝试编写梯度下降的实现，我认为我们非常接近
我们（尝试）按照与this question的第一个答案相关的步骤进行操作，即：
1。计算假设h = X * theta
2. 计算损失= h - y，也许是平方成本（损失^ 2）/ 2m
3。计算渐变= X＆＃39; *损失/ m
4. 更新参数theta = theta - alpha * gradient
但正如您可以从丢失的代码中看到的那样，我们对如何计算渐变感到有些不知所措，我们是否正确设置了它？
如何执行该计算？
X＆＃39;有什么区别？和X？
double loss, cost, hypothesis;
int p, iteration;
iteration = 0;
do
{
iteration++;
cost = 0.0;
//loop through all instances (complete one epoch)
for (p = 0; p < number_of_files__train; p++)
{
hypothesis = calculateHypothesis( weights, feature_matrix__train, p, globo_dict_size );
loss = outputs__train[p] - hypothesis;
for (int i = 0; i < globo_dict_size; i++)
{
weights[i] += LEARNING_RATE * loss * feature_matrix__train[p][i] * calculateGradent( weights, i, number_of_files__train, loss );
}
//summation of squared error (error value for all instances)
cost += (loss*loss);
}
cost = cost/(2 * number_of_files__train);
}
while(cost != 0 && iteration<=MAX_ITER);
}
static double calculateHypothesis( double weights[], double[][] feature_matrix, int file_index, int globo_dict_size )
{
//# m denotes the number of examples here, not the number of features
double sum = 0.0;
for (int i = 0; i < globo_dict_size; i++)
{
sum += ( weights[i] * feature_matrix[file_index][i] );
}
//bias
sum += weights[ globo_dict_size ];
return sigmoid(sum);
}
private static double sigmoid(double x)
{
return 1 / (1 + Math.exp(-x));
}
static double calculateGradent( double weights[], int i, int number_of_files__train, double loss )
{
return weights[i] * loss / number_of_files__train;
}