Ix = sum(Ix,3)/(3*255)
时间: 2023-12-31 21:05:24 浏览: 13
这个表达式的意思是先对矩阵Ix在第三个维度上的所有元素进行求和,得到一个二维矩阵,然后将该二维矩阵中的每个元素除以3乘以255。具体来说,如果Ix的大小为m×n×p,则sum(Ix,3)的大小为m×n,且Ix的每个元素都会除以3乘以255。这个操作相当于对Ix在第三个维度上求平均,然后将结果缩放到[0,1]的范围内,表示为灰度图像。
相关问题
modularity计算模块度的函数实现
计算模块度的函数实现可以根据不同的算法进行选择,以下是一些常用的算法:
1. Newman-Girvan 算法:
```python
def modularity(graph, communities):
m = graph.sum() / 2
Q = 0
for group in communities:
subgraph = graph[np.ix_(group, group)]
lc = subgraph.sum() / 2
dc = subgraph.sum(axis=1)
ec = np.sum(graph[group], axis=0)
Q += lc / m - np.sum(dc * ec) / (2 * m ** 2)
return Q
```
2. Clauset-Newman-Moore 算法:
```python
def modularity(graph, communities):
m = graph.sum() / 2
Q = 0
for group in communities:
subgraph = graph[np.ix_(group, group)]
lc = subgraph.sum() / 2
dc = subgraph.sum(axis=1)
ec = np.sum(graph[group], axis=0)
Q += lc / m - np.sum(dc * ec) / (2 * m ** 2)
return Q
```
3. Blondel-Guillaume-Lambiotte-Lefebvre 算法:
```python
def modularity(graph, communities):
m = graph.sum() / 2
Q = 0
for group in communities:
subgraph = graph[np.ix_(group, group)]
lc = subgraph.sum() / 2
dc = subgraph.sum(axis=1)
ec = np.sum(graph[group], axis=0)
Q += lc / m - (np.sum(dc) / (2 * m)) ** 2 - (np.sum(ec) / (2 * m)) ** 2
return Q
```
这些算法的实现可能有所不同,但基本思想都是一致的。它们根据社区内节点的度数和边权重来计算模块度,从而评估社区的质量和紧密程度。
predictors = read_csv("../data/predictors.csv") pred_vars = c("SD", "OXYGEN", "pH", "TEMPERATURE", "TRIA", "THg", "MICX", # "CHLX", "TURB", "Si", "NO3", "NH3", "TN", "TP", "BENT_MMI", "MMI_ZOOP_NLA6", "RDis_IX", "RVegQc3OE", "DEPTH", "AG_PCT", "URBAN_PCT", "ROADDEN", "POPDEN","DAMDEN_BSN", "MINEDEN_BSN", "RT", "E_I", "PMEAN_BSN", "ELEVMEAN_BSN") X = predictors[,c("UID", pred_vars)] Y = phy_biovolume$ALGAL_GROUP UID = Y$UID apply(Y, 2, function(x){sum(x == 0)/length(x)}) a = apply(Y[, 2:ncol(Y)], 1, sum) Y = Y %>% select(-CYST, -HETEROKONTOPHYTA, 'GOLDEN ALGAE', -'YELLOW-GREEN ALGAE', -EUSTIGMATOPHYTES) %>% select(-UID) res_vars = paste0('V_', 1:ncol(Y)) colnames(Y) = res_vars Y = as.data.frame(t(apply(Y, 1, function(x){round(x/sum(x) * 1000)}))) Y$UID = UID怎么解读
这段代码是用R语言对一些水生植物和浮游生物的数据进行处理和转换。首先,代码读取了一个名为"../data/predictors.csv"的数据文件,并从中选择了一些列作为预测变量,存储在一个名为"pred_vars"的向量中。接着,代码从另一个名为"phy_biovolume"的数据集中选取了一个名为"ALGAL_GROUP"的列,存储在变量"Y"中,并选取了一个名为"UID"的列,存储在变量"UID"中。然后,代码计算了"Y"中每个列中值为0的比例,存储在一个名为"a"的向量中。接下来,代码对"Y"进行了一些数据清洗和转换操作,删除了一些列,并对剩余的列进行了归一化处理,将每个样本的值转换为千分比,并将结果存储在一个名为"Y"的数据框中。最后,代码将"UID"列添加回到"Y"数据框中,得到一个包含归一化后的数据和对应的UID的数据框。