import java.util.Arrays; public class DeepCopy { public static void main(String[] args) { int[] arr1 = {10, 20, 30}; System.out.println("arr1 Before copying: " + Arrays.toString(arr1)); int[] arr2 = new int[arr1.length]; System.arraycopy(arr1, 0, arr2, 0, arr1.length...
所以我的问题是,在克隆状态然后改变它之后,它也会影响先前的状态和所有其他克隆.我的第一个想法是我没有正确创建2d数组的深层副本,但我发现上面的代码没有任何问题. 有什么建议?谢谢 java clone deep-copy Ult*_*ent lucky-day -1推荐指数 1解决办法 201查看次数 java:深度复制列表列表的最佳方法 我正在...
isArray(source)) { const newArray: any[] = []; // (*) newArray: any[] weakMap.set(source, newArray); for (const item of source) { newArray.push(handleDeepCopy(item)); } return newArray as T; } // Set if (source instanceof Set) { const newSet = new Set(); // (*)...
arraycopy(dConnact[pos], head * d_head, heads[head][pos], 0, d_head); } } return heads; } private static float[][] softmax(float[][] input) { float[][] result = new float[input.length][input[0].length]; for (int i = 0; i < input.length; i++) { float max = Floa...
Java源码示例: org.deeplearning4j.nn.params.DefaultParamInitializer 示例1 privatePair<Gradient, INDArray>getGradientsAndDelta(INDArray preOut, LayerWorkspaceMgr workspaceMgr){ ILossFunction lossFunction = layerConf().getLossFn(); INDArray labels2d = getLabels2d(workspaceMgr, ArrayType.BP_WORKING_MEM...
运算符通常包括代数运算符(例如,+,×,exp和topK)、神经网络运算符(例如,卷积和池化)、张量运算符(例如,reshape、resize和copy)、广播和约简运算符(例如,min和argmin),以及控制流运算符(例如,条件和循环)。在这里,我们选择了三个代表性的运算符进行说明。此外,我们讨论了定制运算符的情况。 1)广播:广播运算符...
// Copy strides for (int i = 0; i < length; i++) { _originalStrides[i] = strides[i]; } } } // Standard getters const std::vector<std::unique_ptr<TadTrieNode>>& children() const { return _children; } LongType value() const { return _value; } int level() const { return...
copy() result[result == 0] = -1 # print(data_nozero) # print(kmean_list[i]) label = kmean_list[i].predict(data_nozero).reshape(-1) # print(data_nozero) # print(label) new_data = np.array([kmean_list[i].cluster_centers_[x] for x in label]) data[data != 0] = new...
Determining whether the RNA isoforms from medically relevant genes have distinct functions could facilitate direct targeting of RNA isoforms for disease treatment. Here, as a step toward this goal for neurological diseases, we sequenced 12 postmortem, ag
Compared to the 2D image data, they have multiple-channel dimensions. Meanwhile, the 3D CNN is more complex in computation which requires more computing resources. Thus, it is necessary to consider using lighter network structures or other methods. 3.2 Recurrent neural network Recurrent Neural ...