currentSequence:" + currentSequence); int maxWeight=9;//最大权重 int minWeight=3;//最小权重 int weightSum...int mod = currentSequence % weightSum; System.out.print(" mod:" + mod); for (..., IntegerWrapper> invokerToWeightMap = new LinkedHashMap, IntegerWrapper>(); int weightSum...invokerToWeightMap.put(invokers.get(i), new IntegerWrapper(weight)); weightSum...) int mod = currentSequence % weightSum; for (int i = 0; i < maxWeight; i++)
= 0 && totalWeight > 0.0f) { float weightSum = mWeightSum > 0.0f ?...could absorb extra space -- give him his share int share = (int) (childExtra * delta / weightSum...); weightSum -= childExtra; delta -= share; final int
每个实例 i 除了存在一个配置权重 Wi 外，还存在一个当前有效权重 CWi，且 CWi 初始化为 Wi；指示变量 currentPos 表示当前选择的实例 ID，初始化为 -1；所有实例的配置权重和为 weightSum...； 那么，调度算法可以描述为： 1、初始每个实例 i 的 当前有效权重 CWi 为 配置权重 Wi，并求得配置权重和 weightSum； 2、选出 当前有效权重 最大 的实例，将 当前有效权重 CWi...减去所有实例的 权重和 weightSum，且变量 currentPos 指向此位置； 3、将每个实例 i 的 当前有效权重 CWi 都加上 配置权重 Wi； 4、此时变量 currentPos 指向的实例就是需调度的实例...； 5、每次调度重复上述步骤 2、3、4； 上述 3 个服务，配置权重和 weightSum 为 7，其调度过程如下： 请求 选中前的当前权重 currentPos 选中的实例 选中后当前权重 1 {5
x = 0; x < Width; x++) { unsigned int sum[3] = { 0 }; unsigned int weightsum...sum[c] += weight*sample[c]; } weightsum...for (int c = 0; c < Channels; c++) { LinePD[c] = ClampToByte(sum[c] / weightsum
getEdgesFromAdjacencyList(graph), [i for i in range(graph.number)] sort(edges, 0, len(edges) - 1) weightSum...vertices[beginOrigin] = endOrigin # identify the two vertices in the same sub graph weightSum..., edgeNumber = weightSum + edge.weight, edgeNumber + 1 # calculate the total weight 这里使用 getEdgesFromAdjacencyList...= [{'index': i, 'weight': None} for i in range(graph.number)], [i for i in range(graph.number)] weightSum...= weightSum + vertex['weight'] updateVertices(graph, vertices, verticesIndex, vertex['index'
, Vectors.zeros(numFeatures).toDense))( // 合并在同一个partition中的值 seqOp = { case ((weightSum...featureSum = featureSum + weight × features BLAS.axpy(weight, features, featureSum) (weightSum...+ weight, featureSum) }, //合并不同partition中的值 combOp = { case ((weightSum1,...featureSum1), (weightSum2, featureSum2)) => BLAS.axpy(1.0, featureSum2, featureSum1)...(weightSum1 + weightSum2, featureSum1) }).collect().sortBy(_._1) // label 的类别数，即公式中的 K
GetDefaultSceneTextureUV(Parameters, SceneTextureID); //用于存储累积的颜色 float3 PixelSum = float3(0, 0, 0); //累积权重值 float WeightSum...BlurRadius); //累加颜色 PixelSum += PixelColor * weight; //累加权重值 WeightSum...+= weight; } } //返回加权平均值 return PixelSum / WeightSum; 上面的代码注释，参考：http://opda.tech/2021/01/03/UE4
2.weightSum值 如果我们只有一个按钮，希望占屏幕的50%并且在中间，如下面的效果： 竖屏效果 横屏效果 我们只有一个控件可以设置layout_weight属性，而不管我们设多少，...这时父布局（LinearLayout）中的weightSum属性就可以大显身手了。...weightSum的值就代表父布局的100%总空间，这是我们把LinearLayout的“weightSum”属性设置为“1”，按钮的“layout_weight”设置为“0.5”： <LinearLayout...android:gravity= "center" android:weightSum..."#fdb6b6"/> 其实weightSum
weightSum 这个可以设置整个父控件的比例，android:weightSum="3" , 表示为3个分配，那么下面的布局就是分3分，可1:2,2:1等。
> invokerToWeightMap = new LinkedHashMap, IntegerWrapper>(); // 权重之和 int weightSum...invokerToWeightMap.put(invokers.get(i), new IntegerWrapper(weight)); weightSum...minWeight < maxWeight) { // 将调用次数 % 权重总数，得出偏移量 mod int mod = currentSequence % weightSum
android:background="#BEC0D1" android:text="3" /> 分割占比之和 weightSum...android:weightSum 定义子 view 的 weight 之和的最大值。...如果想给单独的一个子 view 一半的空间占比，可以设置子 view 的 layout_weight 为0.5，并且设置 LinearLayout 的 weightSum 为1.0。...wrap_content" android:layout_marginTop="10dp" android:background="#4DB6AC" android:weightSum
, IntegerWrapper> invokerToWeightMap = new LinkedHashMap, IntegerWrapper>(); int weightSum...invokerToWeightMap.put(invokers.get(i), new IntegerWrapper(weight)); weightSum...sequence.getAndIncrement(); if (maxWeight > 0 && minWeight < maxWeight) { int mod = currentSequence % weightSum
} }) }, //点击抽奖按钮 clickLuck:function(){ var e = this; var weightSum...100 return prev + currVal; //prev 是前一次累加后的数值，currVal 是本次待加的数值 }, 0); console.log(weightSum...); var random = Math.random()*weightSum; console.log(random); var concatWeightArr = e.data.prizeWeight.concat
False ''' Calculate the sum of all particle weights ''' def normalizeWeights(self): weightSum...i in range(self.numParticles): ''' Add the weight of the particles at index 'i' to the weightSum...''' weightSum += self.particles[i].weight ''' Divide each particle's weight by the...for i in range(self.numParticles): self.particles[i].weight = self.particles[i].weight / weightSum
类似 visited 数组的作用，记录哪些节点已经成为最小生成树的一部分 private boolean[] inMST; // 记录最小生成树的权重和 private int weightSum...// 加入横切边队列 pq.offer(edge); } } // 最小生成树的权重和 public int weightSum...() { return weightSum; } // 判断最小生成树是否包含图中的所有节点 public boolean allConnected() {...prim.allConnected()) { // 最小生成树无法覆盖所有节点 return -1; } return prim.weightSum()...int n = points.length; List[] graph = buildGraph(n, points); return new Prim(graph).weightSum
，则用currentSequence对invokers的长度取模得到下标，然后返回该下标对应的invoker；如果不是所有的invoker权重都相同，用三个变量maxWeight、minWeight、weightSum...用currentSequence对weightSum取模得到一个mod，以maxWeight作为外层循环限制，以invoker个数作为内层循环限制，每次循环的时候mod--，对应的invoke权重值--...LinkedHashMap, IntegerWrapper> invokerToWeightMap = new LinkedHashMap(); // 权重总和 int weightSum...> 0) { invokerToWeightMap.put(invokers.get(i), new IntegerWrapper(weight)); weightSum...这时候的权重比才是 1：2：3：4 if (maxWeight > 0 && minWeight < maxWeight) { int mod = currentSequence % weightSum
p); % 制作种群 pop = capacitylimit(pop, capacity, weights,p); % 限制重量 wgtsum = weightsum...selection(pop, sn, profits); % 选择优势个体 p = makep(spop, p, alpha); % 更新概率向量 end wgtsum =weightsum
一些其他函数 重量计算函数： functionwgtsum = weightsum(pop, weights) %计算种群的重量 %pop input 种群 %weights
sigma))/sigma; } void main() { vec2 invSize = 1.0 / uTexSize; float fSigma = float(SIGMA); float weightSum...= gaussianPdf(0.0, fSigma); vec4 diffuseSum = texture2D( uColorTexture, vUv).rgba * weightSum; float...sample2 = texture2D( uColorTexture, vUv - uvOffset).rgba; diffuseSum += (sample1 + sample2) * w; weightSum...+= 2.0 * w; } vec4 result = vec4(1.0) - exp(-diffuseSum/weightSum * uExposure); gl_FragColor
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