ruby machine learning

First, class variablesIn Ruby, you can define a class variable for a class, and the value of a class variable is shared by all instances (objects) of the class.is somewhat similar to static variables in other languages, such as Java, but differs from static variables in Java,Class variables are private and cannot be accessed outside the class, only through the methods of the class.Class variables are identified by a @@ symbol (two consecutive @ symbol

1 # current Block 2 class block 3 def a_method 4 return yield if block_given? 5 'no Block' 6 end 7 end 8 OBJ = block. new 9 puts "# {obj. a_method} "10 puts" # {obj. a_method {"here's a block! "}}" 1 # closure 2 class greeter 3 def initialize (name) 4 @ name = Name 5 end 6 def name 7 @ name 8 end 9 def name = (new_name) 10 @ name = new_name11 end12 end13 G = greeter. new ("Barney") 14 puts G. name 15g. name = "Betty" 16 puts G. name 1 # Switch scope 2 V1 = 1 3 class myclass 4 v2 = 2 5 local_vari

: ContentOn.message do |channel, message|puts"##{channel}: #{message}"Redis.unsubscribeifMessage = ="Exit"End#On.unsubscribe do |channel, subscriptions|puts"unsubscribed from ##{channel} (#{subscriptions} subscriptions)"End EndPS: code from HTTPS://GITHUB.COM/REDIS/REDIS-RB/BLOB/MASTER/EXAMPLES/PUBSUB.RBYou can define various rules in the message to implement the management of the channel.6, here are some methods related to publishing and subscribing.Psubscribe. Subscribes to the given pattern.R

object: Destroy: all associated entities are destroyed delete_all: all associated entities are directly deleted from the database and cannot be recovered nullify: if the foreign key is set to null, restrict_with_exception cannot be restored. if an exception is thrown, the system prompts restrict_with_error. If an error is thrown, the system prompts 5. foreign_key: column name of the foreign key defined. 6. inverse_of: specify the reverse Association object 7. primary_key: Specify the ID of the

Notes: Ruby returns the last value in the method by default.If you need to return more than one value, you can use an array to implement it.A value can be returned by forcing a declaration of return.The basis of the output method is to use puts (output line), print (direct printing).The underlying input method is gets (read in a row, containing ' \ n ' at the end of the line, which can be eliminated by the Chomp method).Pure phenomenon Object languag

Original address: http://www.cnblogs.com/cyruszhu/p/5496913.htmlDo not use for commercial use without permission! For related requests, please contact the author: [Email protected]Reproduced please attach the original link, thank you.1 BasicsL Andrew NG's machine learning video.Connection: homepage, material.L 2.2008-year Andrew Ng CS229 machine LearningOf course

The concept of extreme learning machineElm is a new fast learning algorithm, for TOW layer neural network, elm can randomly initialize input weights and biases and get corresponding output weights.For a single-hidden-layer neural network, suppose there are n arbitrary samples, where。 For a single hidden layer neural network with a hidden layer node, it can be expressed asWhere, for the activation function,

In the introduction of recommendation system, we give the general framework of recommendation system. Obviously, the recommendation method is the most core and key part of the whole recommendation system, which determines the performance of the recommended system to a large extent. At present, the main recommended methods include: Based on content recommendation, collaborative filtering recommendation, recommendation based on association rules, based on utility recommendation, based on knowledge

We have developed a false news detector using machine learning and natural language processing, which has an accuracy rate of more than 95% on the validation set. In the real world, the accuracy rate should be lower than 95%, especially with the passage of time, the way the creation of false news will change. Because of the rapid development of natural language processing and

similar to LWLR, the formula is described in "machine learning combat". The formula adds a coefficient that we set ourselves, and we take 30 different values to see the change of W.STEP5:Ridge return:#岭回归def ridgeregression (data, L): Xmat = Mat (data) Ymat = Mat (l). T Ymean = mean (Ymat, 0) Ymat = Ymat-ymean Xmean = mean (Xmat, 0) v = var (xmat) Xmat = (Xmat-xmean) /V #取30次不同lam岭回

) for in H: Print(i) for in H.flat: print(i)iterating over a multidimensional array is the first axis :if to perform operations on the elements in each array, we can use the flat property, which is an iterator to the array element :Np.flatten () returns an array that is collapsed into one dimension. However, the function can only be applied to the NumPy object, that is , an array or mat, the normal List of lists is not possible. A = Np.array ([[Up], [3, 4], [5, 6]])print(A.flatten

-centralsonatype-oss-snapshots3.1 Production messagesObjectStreamingproducer {DefMain (args:array[String]) {Val random =NewRandom ()Maximum number of events per secondValMaxevents =6Read the list of possible namesVal Namesresource =This.getClass.getResourceAsStream ("/names.csv")Val names = Scala.io.Source.frominputstream (Namesresource). Getlines (). ToList. Head Split (","). ToseqGenerate a sequence of possible productsVal products =Seq ("IPhone Cover"9.99,"Headphones"5.49,"Samsung Galaxy Cove

of the current node is the middle half of the distance of all its leaf nodes is float (NUMLEAFS)/2.0/plottree.totalw* 1, but since the start Plottree.xoff assignment is not starting from 0, but the left half of the table, so also need to add half the table distance is 1/2/plottree.totalw*1, then add up is (1.0 + float (numleafs))/2.0/ Plottree.totalw*1, so the offset is determined, then the X position becomes Plottree.xoff + (1.0 + float (numleafs))/2.0/PLOTTREE.TOTALW3, for Plottree function p

Original address Mathematics is the foundation of computer technology, linear algebra is the basis of machine learning and deep learning, the best way to understand the knowledge of the data I think is to understand the concept, mathematics is not only used for exams in school, but also the essential basic knowledge of the work, in fact, there are many interestin

is all 0. And because it can be deduced that b=1nz∗zt=wt∗ (1NX∗XT) w=wt∗c∗w, this expression actually means that the function of the linear transformation matrix W in the PCA algorithm is to diagonalization the original covariance matrix C. Because diagonalization in linear algebra is obtained by solving eigenvalue and corresponding eigenvector, the process of PCA algorithm can be introduced (the process is mainly excerpted from Zhou Zhihua's "machine