matrix factorization machine learning

(1))) Val Indexrowmatrix = new Indexedrowmatrix (RDD1)//convert Indexedrowmatrix to Blockmatrix, specify the number of rows per block Val Blockmatrix:bloc Kmatrix=indexrowmatrix. Toblockmatrix(2,2)//After the execution of the printed content://index: (0,0) Matrixcontent:2 x 2Cscmatrix//(1,0)20.0//(1,1)30.0Index: (1,1) Matrixcontent:2 x 1Cscmatrix//(0,0)70.0//(1,0)100.0Index: (1,0) Matrixcontent:2 x 2Cscmatrix//(0,0)50.0//(1,0)80.0//(0,1)60.0//(1,1)90.0Index: (0,1) Matrixcontent:2 x 1Cscmatrix//(

(First chapter above)1.2.5 Linalg Linear Algebra LibraryBased on the basic operation of matrices, the Linalg Library of NumPy can satisfy most linear algebra operations.. determinant of matrices. Inverse of the Matrix. Symmetry of matrices. The rank of the matrix. The reversible matrix solves the linear equation1. Determinant of matrices from Import * in[#N-order

, respectively) X = [0 0; 1 0; 0 2] D = pdist (x, 'seuclidean ', [0.5, 1]) Result: D = 2.0000 2.0000 2.8284 6.Markov distance(Mahalanobis distance) (1) Markov distance Definition There are m sample vectors X1 ~ XM, the covariance matrix is recorded as S, and the mean value is recorded as vector μ. Then, the Markov distance between the sample vector X and U is expressed: The Markov distance between the vector XI and XJ is defined: If the covariance

=2.0000 2.0000 2.82846. Markov distance (mahalanobis Distance)(1) Markov distance definitionThere are m sample vectors x1~xm, the covariance matrix is denoted as s, the mean values are denoted as vector μ, and the Markov distances of sample vectors x to u are expressed as:Where the Markov distance between the Vector XI and XJ is defined as:If the covariance matrix is a unit

linear, and for linear irreducible situations it is necessary to take some means to make the data points into linear classification in another dimension, which is not necessarily visual display of the dimension. This method is the kernel function.Using the ' Machine Learning Algorithm (2)-Support vector Machine (SVM) basis ' mentioned: There are no two identical

Turn from:"Basics" Common machine learning Data mining knowledge pointsBasis (Basic):MSE (Mean square error mean squared error), LMS (leastmean square min squared), LSM (Least square Methods least squares), MLE (Maximumlikelihood Estimation maximum likelihood estimation), QP (quadratic programming two-time plan), CP (Conditional probability conditional probability), JP (Joint probability joint probability)

if you have a machine learning problem this problem has multiple special If you can ensure that these features are in a similar range, I mean to make sure that the values of the different features are within a similar range the gradient descent method can converge faster specifically if you have a problem with two features where X1 is the size of the house area Its value is between 0 and 2000 X2 is the n

interview, the interviewer said it felt good and would invite on site for an interview later. Sure enough, two days after the HR phone came, in the Dragon Boat festival after arranging a trip to Suzhou. Side The landlord from Shanghai to Suzhou Microsoft exactly 10 points, the interview arranged at 11 points. I had a little water and a snack at Microsoft's Pantry, and then the first interviewer in charge of the interview took me into the interview room and told me that today's int

as:If the covariance matrix is a unit matrix (the independent distribution of each sample vector), the formula becomes:That's the Euclidean distance.If the covariance matrix is a diagonal matrix, the formula becomes the normalized Euclidean distance.(2) The advantages and disadvantages of Markov distance: dimension in

This is the process of recording self-study, the current theoretical basis is: University advanced mathematics + linear algebra + probability theory. Programming Basics: C/c++,pythonIn watching machine learning combat this book, slowly involved. I believe that the people who have read the above courses can begin to learn machine

First, Introduction In many machine learning and depth learning applications, we find that the most used optimizer is Adam, why? The following is the optimizer in TensorFlow: See also for details: Https://www.tensorflow.org/api_guides/python/train In the Keras also have Sgd,rmsprop,adagrad,adadelta,adam, details: https://keras.io/optimizers/ We can find that in a

to the derivative of the scalar y-to-column vector x,The y is biased for the elements of each x without transpose.DY/DX = [Dy/dx (IJ)]Important Conclusions:y = U ' XV =σσu (i) x (IJ) v (j) then Dy/dx = = UV 'y = U ' X ' XU then dy/dx = 2XUU 'y = (xu-v) ' (xu-v) then dy/dx = d (U ' X ' xu-2v ' XU + V ' V)/dx = 2XUU '-2VU ' + 0 = 2 (xu-v) U '9. Derivative of matrix Y to matrix x:Each element of Y is derivati

algebra runtimeData typeLocated in the Org.apache.spark.mllib package:Vector: Created by the Mllib.linalg.vectors class From bumpy import array from pyspark.mllib.linalg import vectors Create dense vectors Densevec1=array (1.0,2.0.3.0]) #直接传numpy数组 densevec2=verctors.dense ([1.0,2.0,3.0]) Creates a sparse vector that receives only the dimensions of the vector and non-zero position and valueThese locations can be passed with a dictionary, or using two lists that represent the position and valu

Label: style blog HTTP Io ar use for SP strong I. Introduction This document is based on Andrew Ng's machine learning course http://cs229.stanford.edu. In the previous supervised learning regression model, we used the training set to directly model the conditional probability P (Y | X; θ). For example, Logistic Regression uses hθ (X) = g (θ Tx) Modeling P (

(mahalanobis Distance)(1) Markov distance definitionThere are m sample vectors x1~xm, the covariance matrix is denoted as s, the mean values are denoted as vector μ, and the Markov distances of sample vectors x to u are expressed as:Where the Markov distance between the Vector XI and XJ is defined as:If the covariance matrix is a unit matrix (the independent dis

machine learning consists of three stages : First stage: Learning model . Using the learning algorithm, the classification model is obtained by inductive learning of the training set. Phase two: test the Model . The classification models that have been learned a

An Introduction to "Iterative Methods" in Machine Learning" Zouxy09@qq.com Http://blog.csdn.net/zouxy09 First, let's take a look at the eight-part article (from Baidu encyclopedia): the iterative method, also known as the tossing method, is a process of constantly using the old value of the variable to recursive the new value, what corresponds to the iteration method is a direct method (or a solution), tha

8 tactics to Combat imbalanced Classes on Your machine learning Datasetby Jason Brownlee on August learning ProcessHave this happened?You is working on your dataset. You create a classification model and get 90% accuracy immediately. "Fantastic" you think. You dive a little deeper and discover this 90% of the data belongs to one class. damn!This is a example of a

a hypothetical function, which is more realistic: Vi. normal equation (normal equation)For some linear regression problems, it is better to use the normal equation to solve the optimal value of the parameter θ . For the gradient descent method we are currently using, J (θ) needs several iterations to converge to the minimum value. The normal equation method provides an analytic solution for θ , that is, the solution is solved directly, and the optimal value is obtained in one step. The key po

reduced after removing the label, (2) using the data of the reduced dimension to train the model, (3) for the new data points, the PCA reduced dimension to obtain the dimensionality reduction data, and the model to obtain the predicted value. Note : You should only use the training set data for PCA dimensionality reduction get Map $x^{(i)}\rightarrow z^{(i)}$, and then apply the mapping (PCA-selected principal matrix $u_reduce$) to the validation set