reduce

问题 Given the following code: KStream<String, Custom> stream = builder.stream(Serdes.String(), customSerde, "test_in"); stream .groupByKey(Serdes.String(), customSerde) .reduce(new CustomReducer(), "reduction_state") .print(Serdes.String(), customSerde); I have a println statement inside the apply method of the Reducer, which successfully prints out when I expect the reduction to take place. However, the final print statement shown above displays nothing. likewise if I use a to method rather than

问题 I know this question might sound a little bit crazy, but I tough that maybe someone could come up with a smart idea: Imagine you have 1000 thumbnail images on a single HTML page. The image size is about 5-10 kb. Is there a way to load all images in a single request? Somehow zip all images into a single file… Or do you have any other suggestions in the subject? Other options I already know of: CSS sprites Lazy load Set Expire headers Downloads images across different hostnames 回答1: There are

问题 So assume I have the following array of objects: var arr = [ {"name": "John", "score": "8.8"}, {"name": "John", "score": "8.6"}, {"name": "John", "score": "9.0"}, {"name": "John", "score": "8.3"}, {"name": "Tom", "score": "7.9"} ]; var count = 0; var avgScore = arr.reduce(function (sum,person) { if (person.name == "John") { count+=1; return sum + parseFloat(person.score); } return sum; },0)/count); Question: Is there a way way to calculate the average score for "John" without creating a

问题 I am learning tensorflow, I picked up the following code from the tensorflow website. According to my understanding, axis=0 is for rows and axis=1 is for columns. How are they getting output mentioned in comments? I have mentioned output according to my thinking against ##. import tensorflow as tf x = tf.constant([[1, 1, 1], [1, 1, 1]]) tf.reduce_sum(x, 0) # [2, 2, 2] ## [3, 3] tf.reduce_sum(x, 1) # [3, 3] ##[2, 2, 2] tf.reduce_sum(x, [0, 1]) # 6 ## Didn't understood at all. 回答1: x has a

问题 I am learning tensorflow, I picked up the following code from the tensorflow website. According to my understanding, axis=0 is for rows and axis=1 is for columns. How are they getting output mentioned in comments? I have mentioned output according to my thinking against ##. import tensorflow as tf x = tf.constant([[1, 1, 1], [1, 1, 1]]) tf.reduce_sum(x, 0) # [2, 2, 2] ## [3, 3] tf.reduce_sum(x, 1) # [3, 3] ##[2, 2, 2] tf.reduce_sum(x, [0, 1]) # 6 ## Didn't understood at all. 回答1: x has a

问题 In ES5, the new array method reduce(). I am wondering if someone can explain more in depth. var test = [1, 2, 3, 4, 5].reduce(function(inital, item, idx) { return inital + item }, 0); console.log(test); In this example, we know for a fact that initial argument is 0 and loops through with callback function. Can someone explain it to me the scopes of initial argument? If we assume that [1, 2, 3, 4, 5] is what is returned from the imaginary method and want to return an object form which key is

问题 I need to get the next lower object in an array using a weight value. const data = [ { weight: 1, size: 2.5 }, { weight: 2, size: 3.0 }, { weight: 4, size: 3.5 }, { weight: 10, size: 4.0 }, { weight: 20, size: 5.0 }, { weight: 30, size: 6.0 } ] If the weight is 19, I need to get the object { weight: 10, size: 4.0 } . With this attempt, I do get the closest object, but I always need to get the object with the next lowest value. If weight is smaller then 1, the first element should be returned.

问题 I'm looking at this example of use of reduce() function. function add(runningTotal, currentValue) { return runningTotal + currentValue; } var nums = [1,2,3,4,5,6,7,8,9,10]; var sum = nums.reduce(add); print(sum); // displays 55 Could you give show me some other examples of using reduce() - I'm not sure I fully follow how it works. Thank you 回答1: What reduces does is take an initialValue , a function with 2 essential parameters (can take more) and a list of values. If no initialValue is

问题 I want to left_join multiple data frames: dfs <- list( df1 = data.frame(a = 1:3, b = c("a", "b", "c")), df2 = data.frame(c = 4:6, b = c("a", "c", "d")), df3 = data.frame(d = 7:9, b = c("b", "c", "e")) ) Reduce(left_join, dfs) # a b c d # 1 1 a 4 NA # 2 2 b NA 7 # 3 3 c 5 8 This works because they all have the same b column, but Reduce doesn't let me specify additional arguments that I can pass to left_join . Is there a work around for something like this? dfs <- list( df1 = data.frame(a = 1:3

问题 I want to left_join multiple data frames: dfs <- list( df1 = data.frame(a = 1:3, b = c("a", "b", "c")), df2 = data.frame(c = 4:6, b = c("a", "c", "d")), df3 = data.frame(d = 7:9, b = c("b", "c", "e")) ) Reduce(left_join, dfs) # a b c d # 1 1 a 4 NA # 2 2 b NA 7 # 3 3 c 5 8 This works because they all have the same b column, but Reduce doesn't let me specify additional arguments that I can pass to left_join . Is there a work around for something like this? dfs <- list( df1 = data.frame(a = 1:3