reduce

问题 I'm doing a little deep learning, and I want to grab the values of all hidden layers. So I end up writing functions like this: def forward_pass(x, ws, bs): activations = [] u = x for w, b in zip(ws, bs): u = np.maximum(0, u.dot(w)+b) activations.append(u) return activations If I didn't have to get the intermediate values, I'd use the much less verbose form: out = reduce(lambda u, (w, b): np.maximum(0, u.dot(w)+b), zip(ws, bs), x) Bam. All one line, nice and compact. But I can't keep any of

问题 I'm doing a little deep learning, and I want to grab the values of all hidden layers. So I end up writing functions like this: def forward_pass(x, ws, bs): activations = [] u = x for w, b in zip(ws, bs): u = np.maximum(0, u.dot(w)+b) activations.append(u) return activations If I didn't have to get the intermediate values, I'd use the much less verbose form: out = reduce(lambda u, (w, b): np.maximum(0, u.dot(w)+b), zip(ws, bs), x) Bam. All one line, nice and compact. But I can't keep any of

问题 I have a MultivaluedMap and a list of strings, and I would like to see which of those string are keys in the MultivaluedMap . For each of the strings that are keys in the MultivaluedMap , I want to construct a new Thing out of the value of that key, set that string as a new key in a new HashMap<String, Thing> , and set the new Thing I've created as the value for that new key in the HashMap . Right now, using a vanilla forEach , I have the following working solution: MultivaluedMap<String,

问题 I am trying to sum the unit value by date, and create a new array where there are no duplicate dates. For example, I want to calculate the total of 2015-12-04 00:01:00 . This date has occurred 2 times in the following data, its value is 5 and 6 , which is going to be: [{date: '2015-12-04 00:01:00', unit: 11}, ... etc] I have tried arr = results.map(x => x.unit).reduce((a,c) => a + c) but it only return a single value, not an array. results = [ { unit: 5, date: '2015-12-04 00:01:00' }, { unit:

问题 I am trying to sum the unit value by date, and create a new array where there are no duplicate dates. For example, I want to calculate the total of 2015-12-04 00:01:00 . This date has occurred 2 times in the following data, its value is 5 and 6 , which is going to be: [{date: '2015-12-04 00:01:00', unit: 11}, ... etc] I have tried arr = results.map(x => x.unit).reduce((a,c) => a + c) but it only return a single value, not an array. results = [ { unit: 5, date: '2015-12-04 00:01:00' }, { unit:

问题 I'm working with apps script. I have an array of objects 'sendableRows' that I would like to turn to json and email. an object looks like: [{Phone Number=14444444444, Eagerness=High, Index=4816.0, completed=, Lot Size=0.74, Power or water=, campaign=, absoluteRow=84.0}] my code: const json = sendableRows.reduce(row => JSON.stringify(row), "") Logger.log(json); MailApp.sendEmail({ to: 'xxxx@gmail.com', subject: todayString, htmlBody: json }); unfortunately 'json' is being output as: [20-07-26

问题 Cheerz, Recently I have being trying out Spark and do far I have observed quite interesting results, but currently I am stuck with famous groupByKey OOM problem. Basically what the job does it tries to search in the large datasets the periods where measured value is increasing consecutively for at least N times. I managed to get rid of the problem by writing the results to the disk, but the application is running much slower now (which is expected due to the disk IO). Now the question: is

问题 Cheerz, Recently I have being trying out Spark and do far I have observed quite interesting results, but currently I am stuck with famous groupByKey OOM problem. Basically what the job does it tries to search in the large datasets the periods where measured value is increasing consecutively for at least N times. I managed to get rid of the problem by writing the results to the disk, but the application is running much slower now (which is expected due to the disk IO). Now the question: is

问题 Cheerz, Recently I have being trying out Spark and do far I have observed quite interesting results, but currently I am stuck with famous groupByKey OOM problem. Basically what the job does it tries to search in the large datasets the periods where measured value is increasing consecutively for at least N times. I managed to get rid of the problem by writing the results to the disk, but the application is running much slower now (which is expected due to the disk IO). Now the question: is

问题 Cheerz, Recently I have being trying out Spark and do far I have observed quite interesting results, but currently I am stuck with famous groupByKey OOM problem. Basically what the job does it tries to search in the large datasets the periods where measured value is increasing consecutively for at least N times. I managed to get rid of the problem by writing the results to the disk, but the application is running much slower now (which is expected due to the disk IO). Now the question: is