artificial-intelligence

I've read about neural network a little while ago and I understand how an ANN (especially a multilayer perceptron that learns via backpropagation) can learn to classify an event as true or false. I think there are two ways : 1) You get one output neuron. It it's value is > 0.5 the events is likely true, if it's value is <=0.5 the event is likely to be false. 2) You get two output neurons, if the value of the first is > than the value of the second the event is likely true and vice versa. In these case, the ANN tells you if an event is likely true or likely false. It does not tell how likely it

Is there any way to import a JSON file (contains 100 documents) in elasticsearch server? I want to import a big json file into es-server.. You should use Bulk API . Note that you will need to add a header line before each json document. $ cat requests { "index" : { "_index" : "test", "_type" : "type1", "_id" : "1" } } { "field1" : "value1" } $ curl -s -XPOST localhost:9200/_bulk --data-binary @requests; echo {"took":7,"items":[{"create":{"_index":"test","_type":"type1","_id":"1","_version":1,"ok":true}}]} As dadoonet already mentioned, the bulk API is probably the way to go. To transform your

while I'm reading in how to build ANN in pybrain , they say: Train the network for some epochs. Usually you would set something like 5 here, trainer.trainEpochs( 1 ) I looked for what is that mean , then I conclude that we use an epoch of data to update weights, If I choose to train the data with 5 epochs as pybrain advice, the dataset will be divided into 5 subsets, and the wights will update 5 times as maximum. I'm familiar with online training where the wights are updated after each sample data or feature vector, My question is how to be sure that 5 epochs will be enough to build a model

I'm working in a sentiment analysis problem the data looks like this: label instances 5 1190 4 838 3 239 1 204 2 127 So my data is unbalanced since 1190 instances are labeled with 5 . For the classification Im using scikit's SVC . The problem is I do not know how to balance my data in the right way in order to compute accurately the precision, recall, accuracy and f1-score for the multiclass case. So I tried the following approaches: First: wclf = SVC(kernel='linear', C= 1, class_weight={1: 10}) wclf.fit(X, y) weighted_prediction = wclf.predict(X_test) print 'Accuracy:', accuracy_score(y_test,

I am looking for a method on how to calculate the number of layers and the number of neurons per layer. As input i only have the size of the input vector, the size of the output vector and the size of the trainig set. Usually the best net is determined by trying different net topologies and selecting the one with the least error. Unfortunately I can not do that. This is a really hard problem. The more internal structure a network has, the better that network will be at representing complex solutions. On the other hand, too much internal structure is slower, may cause training to diverge, or

I'm trying to make a stock market simulator (perhaps eventually growing into a predicting AI), but I'm having trouble finding data to use. I'm looking for a (hopefully free) source of historical stock market data. Ideally, it would be a very fine-grained (second or minute interval) data set with price and volume of every symbol on NASDAQ and NYSE (and perhaps others if I get adventurous). Does anyone know of a source for such info? I found this question which indicates Yahoo offers historical data in CSV format, but I've been unable to find out how to get it in a cursory examination of the