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norm | Access a database in one line of code | SQL Database library

 by   dieselpoint Java Version: 1.0 License: Non-SPDX

 by   dieselpoint Java Version: 1.0 License: Non-SPDX

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kandi X-RAY | norm Summary

norm is a Java library typically used in Database, SQL Database, Hibernate applications. norm has no bugs, it has no vulnerabilities, it has build file available and it has high support. However norm has a Non-SPDX License. You can download it from GitHub, Maven.
Norm is an extremely lightweight layer over JDBC. It gets rid of large amounts of boilerplate JDBC code. It steals some ideas from ActiveJDBC, which is a very nice system, but requires some very ugly instrumentation / byte code rewriting.
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kandi-support Support

  • norm has a highly active ecosystem.
  • It has 155 star(s) with 23 fork(s). There are 16 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 15 open issues and 26 have been closed. On average issues are closed in 107 days. There are 2 open pull requests and 0 closed requests.
  • It has a positive sentiment in the developer community.
  • The latest version of norm is 1.0
norm Support
Best in #SQL Database
Average in #SQL Database
norm Support
Best in #SQL Database
Average in #SQL Database

quality kandi Quality

  • norm has 0 bugs and 0 code smells.
norm Quality
Best in #SQL Database
Average in #SQL Database
norm Quality
Best in #SQL Database
Average in #SQL Database

securitySecurity

  • norm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • norm code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
norm Security
Best in #SQL Database
Average in #SQL Database
norm Security
Best in #SQL Database
Average in #SQL Database

license License

  • norm has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
norm License
Best in #SQL Database
Average in #SQL Database
norm License
Best in #SQL Database
Average in #SQL Database

buildReuse

  • norm releases are available to install and integrate.
  • Deployable package is available in Maven.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
  • norm saves you 881 person hours of effort in developing the same functionality from scratch.
  • It has 2016 lines of code, 181 functions and 27 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
norm Reuse
Best in #SQL Database
Average in #SQL Database
norm Reuse
Best in #SQL Database
Average in #SQL Database
Top functions reviewed by kandi - BETA

kandi has reviewed norm and discovered the below as its top functions. This is intended to give you an instant insight into norm implemented functionality, and help decide if they suit your requirements.

  • Returns the results of a select statement .
    • Creates a SQL statement to create a table for a given class .
      • Populate the properties .
        • Get data source .
          • Wrap a primitive type .
            • Makes INSERT INSERT UPDATE .
              • Convert array of Integer to Integer .
                • Rolls back the database connection .
                  • Get the base type .
                    • Gets the SQL .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      norm Key Features

                      Access a database in one line of code.

                      norm Examples and Code Snippets

                      See all related Code Snippets

                      Norm

                      copy iconCopydownload iconDownload
                      List<Person> people = db.where("name=?", "Bob").results(Person.class);
                      

                      Sample Code

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                      Database db = new Database();
                      
                      Person joe = new Person();
                      joe.firstName = "Joe";
                      joe.lastName = "Sixpack";
                      
                      db.insert(joe);
                      
                      List<Person> people = db.where("lastname=?", "Sixpack").orderBy("lastName").results(Person.class);
                      

                      Maps and Lists

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                      List<Map> list = db.sql("select * from people").results(HashMap.class);
                      

                      Primitives

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                      Long count = db.sql("select count(*) from people").first(Long.class);
                      

                      Annotations

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                      @Table(name="people")
                      public class Person {
                      	@Id
                      	@GeneratedValue
                      	public long personId;
                      
                      	public String name;
                      
                      	@Column(name = "theColumnName")
                      	public String renameThis;
                      
                      	@Transient
                      	public String thisFieldGetsIgnored;
                      }
                      
                      

                      Transactions

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                      Transaction trans = db.startTransaction();
                      try {
                      	db.transaction(trans).insert(row1);
                      	db.transaction(trans).update(row2);
                      	trans.commit();
                      } catch (Throwable t) {
                      	trans.rollback();
                      } 
                      
                      

                      Pluggable SQL Flavors

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                      Database db = new Database();
                      db.setSqlMaker(new MySqlMaker());
                      

                      Configuration

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                      <dependency>
                          <groupId>com.dieselpoint</groupId>
                          <artifactId>norm</artifactId>
                          <version>0.8.5</version>
                      </dependency>
                      

                      Dependencies

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                      <dependency>
                          <groupId>mysql</groupId>
                          <artifactId>mysql-connector-java</artifactId>
                          <version>8.0.15</version>
                      </dependency>
                      

                      Does `table` round numeric values?

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                      x = sqrt(2)
                      print(x, digits = 22)
                      # [1] 1.414213562373095145475
                      as.character(x)
                      # [1] "1.4142135623731"
                      
                      x = c(pi, pi + 1e-15)
                      
                      x == pi
                      # [1]  TRUE FALSE
                      
                      as.character(x)
                      # [1] "3.14159265358979" "3.14159265358979"
                      
                      table(x)
                      # x
                      # 3.14159265358979 
                      #                2 
                      
                      x = sqrt(2)
                      print(x, digits = 22)
                      # [1] 1.414213562373095145475
                      as.character(x)
                      # [1] "1.4142135623731"
                      
                      x = c(pi, pi + 1e-15)
                      
                      x == pi
                      # [1]  TRUE FALSE
                      
                      as.character(x)
                      # [1] "3.14159265358979" "3.14159265358979"
                      
                      table(x)
                      # x
                      # 3.14159265358979 
                      #                2 
                      

                      How can I make a Shiny app W3C compliant?

                      copy iconCopydownload iconDownload
                      # Reprex adapted from https://shiny.rstudio.com/gallery/tabsets.html
                      library(shiny)
                      library(htmltools)
                      
                      # Define UI for random distribution app ----
                      ui <- fluidPage(
                        
                        # App title ----
                        titlePanel("Tabsets"),
                        
                        # Sidebar layout with input and output definitions ----
                        sidebarLayout(
                          
                          # Sidebar panel for inputs ----
                          {querySidebarPanel <- tagQuery(sidebarPanel(
                            # Input: Select the random distribution type ----
                            radioButtons("dist", "Distribution type:",
                                         c("Normal" = "norm",
                                           "Uniform" = "unif",
                                           "Log-normal" = "lnorm",
                                           "Exponential" = "exp")),
                            
                            # br() element to introduce extra vertical spacing ----
                            br(),
                            
                            # Input: Slider for the number of observations to generate ----
                            sliderInput("n",
                                        "Number of observations:",
                                        value = 500,
                                        min = 1,
                                        max = 1000)
                          ))
                          querySidebarPanel$find(".well")$removeAttrs("role")$addAttrs("role" = "none")$allTags()},
                          
                          # Main panel for displaying outputs ----
                          mainPanel(
                            
                            # Output: Tabset w/ plot, summary, and table ----
                            tabsetPanel(type = "tabs",
                                        tabPanel("Plot", plotOutput("plot")),
                                        tabPanel("Summary", verbatimTextOutput("summary")),
                                        tabPanel("Table", tableOutput("table"))
                            )
                            
                          )
                        )
                      )
                      
                      # Define server logic for random distribution app ----
                      server <- function(input, output) {
                        
                        # Reactive expression to generate the requested distribution ----
                        d <- reactive({
                          dist <- switch(input$dist,
                                         norm = rnorm,
                                         unif = runif,
                                         lnorm = rlnorm,
                                         exp = rexp,
                                         rnorm)
                          
                          dist(input$n)
                        })
                        
                        # Generate a plot of the data ----
                        output$plot <- renderPlot({
                          dist <- input$dist
                          n <- input$n
                          
                          hist(d(),
                               main = paste("r", dist, "(", n, ")", sep = ""),
                               col = "#75AADB", border = "white")
                        })
                      }
                      
                      # Create Shiny app ----
                      shinyApp(ui, server)
                      

                      Is there a way to speed up looping over numpy.where?

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                      background = np.max(segmap)
                      mask = segmap != background
                      objects = segmap[mask]
                      uniqueObjects, counts = np.unique(objects, return_counts=True)
                      ordering = np.argsort(objects)
                      i, j = np.where(mask)
                      indices = np.vstack([i[ordering], j[ordering]])
                      indicesPerObject = np.split(indices, counts.cumsum()[:-1], axis=1)
                      
                      objects = np.empty(uniqueObjects.size, dtype=[('idx', 'i4'), ('pixels', 'O')])
                      objects['idx'] = uniqueObjects
                      for i in range(uniqueObjects.size):
                          # Use `tuple(...)` to get the exact same type as the initial code here
                          objects[i]['pixels'] = tuple(indicesPerObject[i])
                      # In case the conversion to tuple is not required, the loop can also be accelerated:
                      # objects['pixels'] = indicesPerObject
                      

                      Implementation of the Metropolis-Hasting algorithm for solving gaussian integrals

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                      import numpy as np
                      from scipy.stats import norm
                      
                      
                      """
                      We want to get an exponential decay integral approx using importance sampling.
                      We will try to integrate x^2exp(-x^2) over the real line.
                      Metropolis-hasting alg will generate configuartions (in this case, single numbers) such that 
                      the probablity of a given configuration x^a ~ p(x^a) for p(x) propto exp(-x^2).
                      
                      Once configs  = {x^a} generated, the apporximation, Q_N, of the integral, I, will be given by 
                      Q_N = 1/N sum_(configs) x^2
                      lim (N-> inf) Q_N -> I
                      """
                      
                      """
                      Implementing metropolis-hasting algorithm
                      """
                      
                      # Setting up the initial config for our chain
                      x_0 = np.random.uniform(-20, -10)
                      
                      # Defining function that generates the next N steps in the chain, given a starting config x
                      # Works by iteratively taking the last element in the chain, generating a new candidate configuration from it and accepting/rejecting according to the algorithm
                      # Success and failures implemented to see roughly the success rate of each step
                      def next_steps(x, N):
                          Success = 0
                          Failures = 0
                          Data = np.empty((N,))
                          d = 1.5  # Spread of (normal) transition function
                          for i in range(N):
                              r = np.random.uniform(0, 1)
                              delta = np.random.normal(0, d)
                              x_new = x + delta
                              hasting_ratio = np.exp(-(x_new ** 2 - x ** 2))
                              if hasting_ratio > r:
                                  x = x_new
                                  Success = Success + 1
                              else:
                                  Failures = Failures + 1
                              Data[i] = x
                          print(Success)
                          print(Failures)
                          return Data
                      
                      
                      # Number of steps in the chain
                      N_iteration = 50000
                      
                      # Generating the data
                      Data = next_steps(x_0, N_iteration)
                      
                      # Obtaining tail end data and obtaining the standard deviation of resulting gaussian distribution
                      Data = Data[-40000:]
                      (mu, sigma) = norm.fit(Data)
                      print(sigma)
                      

                      Efficient way to map 3D function to a meshgrid with NumPy

                      copy iconCopydownload iconDownload
                      F_sorted = F[np.lexsort((F[:,0], F[:,1], F[:,2]))]  # sort by x, then y, then z
                      
                      F_values = F_sorted[:, 3]
                      
                      M = F_sorted.reshape(N, N, N)
                      
                      F_sorted = F[np.lexsort((F[:,0], F[:,1], F[:,2]))]  # sort by x, then y, then z
                      
                      F_values = F_sorted[:, 3]
                      
                      M = F_sorted.reshape(N, N, N)
                      
                      F_sorted = F[np.lexsort((F[:,0], F[:,1], F[:,2]))]  # sort by x, then y, then z
                      
                      F_values = F_sorted[:, 3]
                      
                      M = F_sorted.reshape(N, N, N)
                      
                      data = np.array([[1, 2, 3], #14 (corner1)
                                       [4, 5, 6], #77 (corner2)
                                       [2.5, 3.5, 4.5], #38.75 (duplicated pixel)
                                       [2.9, 3.9, 4.9], #47.63 (duplicated pixel)
                                       [1.5, 2, 3]]) #15.25 (one step up from [1, 2, 3])
                      step = 0.5                            
                      data_idx = ((data - data.min(axis=0))//step).astype(int)
                      M = np.zeros(np.max(data_idx, axis=0) + 1)
                      x, y, z = data_idx.T
                      M[x, y, z] = F
                      
                      step = 0.1
                      mins = np.min(data, axis=0)
                      maxs = np.max(data, axis=0)
                      ranges = np.floor((maxs - mins) / step + 1).astype(int)
                      indx = np.zeros(data.shape,dtype=int)
                      for i in range(3):
                          x = np.linspace(mins[i], maxs[i], ranges[i])
                          indx[:,i] = np.argmax(data[:,i,np.newaxis] <= (x[np.newaxis,:]), axis=1) -1
                          
                      M = np.zeros(ranges)
                      M[indx[:,0],indx[:,1],indx[:,2]] = F
                      

                      How do I edit the WHERE clause of my VBA SQL-statement so I returns the records from more than 1 field?

                      copy iconCopydownload iconDownload
                      cmdString = "SELECT DISTINCT t.rapportnaam, t.norm, t.analysenaam" & _
                      " FROM taken t, monsters m, opdrachten o" & _
                      " WHERE o.labnummer = " & [LabNumberPrimary] & _
                      " OR o.labnummer = " & [LabNumber_4_CT] & _
                      " AND m.opdrachtteller = o.opdrachtteller" & _
                      " AND t.monsterteller = m.monsterteller"
                      
                      LabNumberPrimary = Nz([LabNumberPrimary])
                      LabNumber_3_ASB = Nz([LabNumber_3_ASB])
                      LabNumber_4_CT = Nz([LabNumber_4_CT])
                      
                      cmdString = "SELECT DISTINCT t.rapportnaam, t.norm, t.analysenaam FROM taken AS t, monsters AS m, opdrachten AS o WHERE (((o.labnummer)='" & LabNumberPrimary & "' Or (o.labnummer)='" & LabNumber_3_ASB & "' Or (o.labnummer)='" & LabNumber_4_CT & "') AND ((m.opdrachtteller)=[o].[opdrachtteller]) AND ((t.monsterteller)=[m].[monsterteller]))"
                      

                      Phase portrait of Verhulst equation

                      copy iconCopydownload iconDownload
                      import numpy as np
                      from scipy.integrate import odeint
                      import matplotlib.pyplot as plt
                      
                      fig = plt.figure(figsize=(8, 4), dpi= 80, facecolor='whitesmoke', edgecolor='k')
                      beta, delta, gamma = 1, 2, 1
                      b,d,c = 1,2,1
                      
                      t = np.linspace(0, 10, 100)
                      
                      C1 = gamma*c-delta*d
                      C2 = gamma*b-beta*d
                      C3 = beta*c-delta*b
                      
                      def verhulst(X, t=0):
                          return np.array([beta*X[0] - delta*X[0]**2 -gamma*X[0]*X[1],
                                           b*X[1] - d*X[1]**2 -c*X[0]*X[1]])
                      
                      X_O = np.array([0., 0.])
                      X_R = np.array([C2/C1, C3/C1])
                      X_P = np.array([0, b/d])
                      X_Q = np.array([beta/delta, 0])
                      
                      def jacobian(X, t=0):
                          return np.array([[beta-delta*2*X[0]-gamma*X[1],  -gamma*x[0]],
                                           [b-d*2*X[1]-c*X[0],             -c*X[1]]])
                      
                      values  = np.linspace(0.05, 0.15, 5)                      
                      vcolors = plt.cm.autumn_r(np.linspace(0.3, 1., len(values)))
                      
                      
                      for v, col in zip(values, vcolors):
                          X0 = [v,0.2-v]
                          X = odeint(verhulst, X0, t)
                          plt.plot(X[:,0], X[:,1], color=col, label='X0=(%.f, %.f)' % ( X0[0], X0[1]) )
                      
                      for v, col in zip(values, vcolors):
                          X0 = [6 * v, 6 *(0.2-v)]
                          X = odeint(verhulst, X0, t)
                          plt.plot(X[:,0], X[:,1], color=col, label='X0=(%.f, %.f)' % ( X0[0], X0[1]) )
                      
                          
                      ymax = plt.ylim(ymin=0)[1] 
                      xmax = plt.xlim(xmin=0)[1]
                      nb_points = 20
                      
                      x = np.linspace(0, xmax, nb_points)
                      y = np.linspace(0, ymax, nb_points)
                      
                      X1, Y1  = np.meshgrid(x, y)
                      DX1, DY1 = verhulst([X1, Y1])  # compute growth rate on the gridt
                      M = (np.hypot(DX1, DY1))       # Norm of the growth rate
                      M[M == 0] = 1.                 # Avoid zero division errors
                      DX1 /= M                       # Normalize each arrows
                      DY1 /= M
                      
                      plt.quiver(X1, Y1, DX1, DY1, M, cmap=plt.cm.jet)
                      plt.xlabel('Number of Species 1')
                      plt.ylabel('Number of Species 2')
                      plt.grid()
                      
                      

                      Different behaviours when initializing differently

                      copy iconCopydownload iconDownload
                      y = R.triangularView<Eigen::Upper>().solve(b);
                      x << R.triangularView<Eigen::Upper>().solve(b);
                      
                      R += 10*MatrixXd::Identity(n,n);
                      
                      #include <iostream>
                      #include <Eigen/Dense>
                      #include <Eigen/SVD>
                      
                      using Eigen::MatrixXd;
                      using Eigen::VectorXd;
                      using Eigen::BDCSVD;
                      
                      int main()
                      {
                        const unsigned int n = 25;
                        MatrixXd R = MatrixXd::Random(n,n);
                        VectorXd b = VectorXd::Random(n);
                      
                        VectorXd x = VectorXd::Zero(n);
                        VectorXd y = VectorXd::Zero(n);
                      
                        // Uncomment to reduce the condition number
                        // R += 10*MatrixXd::Identity(n,n);
                      
                        y = R.triangularView<Eigen::Upper>().solve(b);
                        x << R.triangularView<Eigen::Upper>().solve(b);
                      
                        std::cout << "res(x): " << (b - R.triangularView<Eigen::Upper>() * x).norm() << std::endl;
                        std::cout << "res(y): " << (b - R.triangularView<Eigen::Upper>() * y).norm() << std::endl;
                      
                        Eigen::BDCSVD<Eigen::MatrixXd> svd(R.triangularView<Eigen::Upper>());
                        VectorXd sigma = svd.singularValues();
                        std::cout << "cond: " << sigma.maxCoeff() / sigma.minCoeff() << std::endl;
                      
                        std::cout << "error norm: " << (x-y).norm() << std::endl;
                        std::cout << "error max: " << (x-y).lpNorm<Eigen::Infinity>() << std::endl;
                      
                        return 0;
                      }
                      
                       x << VectorXd(R.triangularView<Eigen::Upper>().solve(b));
                      
                      y = R.triangularView<Eigen::Upper>().solve(b);
                      x << R.triangularView<Eigen::Upper>().solve(b);
                      
                      R += 10*MatrixXd::Identity(n,n);
                      
                      #include <iostream>
                      #include <Eigen/Dense>
                      #include <Eigen/SVD>
                      
                      using Eigen::MatrixXd;
                      using Eigen::VectorXd;
                      using Eigen::BDCSVD;
                      
                      int main()
                      {
                        const unsigned int n = 25;
                        MatrixXd R = MatrixXd::Random(n,n);
                        VectorXd b = VectorXd::Random(n);
                      
                        VectorXd x = VectorXd::Zero(n);
                        VectorXd y = VectorXd::Zero(n);
                      
                        // Uncomment to reduce the condition number
                        // R += 10*MatrixXd::Identity(n,n);
                      
                        y = R.triangularView<Eigen::Upper>().solve(b);
                        x << R.triangularView<Eigen::Upper>().solve(b);
                      
                        std::cout << "res(x): " << (b - R.triangularView<Eigen::Upper>() * x).norm() << std::endl;
                        std::cout << "res(y): " << (b - R.triangularView<Eigen::Upper>() * y).norm() << std::endl;
                      
                        Eigen::BDCSVD<Eigen::MatrixXd> svd(R.triangularView<Eigen::Upper>());
                        VectorXd sigma = svd.singularValues();
                        std::cout << "cond: " << sigma.maxCoeff() / sigma.minCoeff() << std::endl;
                      
                        std::cout << "error norm: " << (x-y).norm() << std::endl;
                        std::cout << "error max: " << (x-y).lpNorm<Eigen::Infinity>() << std::endl;
                      
                        return 0;
                      }
                      
                       x << VectorXd(R.triangularView<Eigen::Upper>().solve(b));
                      
                      y = R.triangularView<Eigen::Upper>().solve(b);
                      x << R.triangularView<Eigen::Upper>().solve(b);
                      
                      R += 10*MatrixXd::Identity(n,n);
                      
                      #include <iostream>
                      #include <Eigen/Dense>
                      #include <Eigen/SVD>
                      
                      using Eigen::MatrixXd;
                      using Eigen::VectorXd;
                      using Eigen::BDCSVD;
                      
                      int main()
                      {
                        const unsigned int n = 25;
                        MatrixXd R = MatrixXd::Random(n,n);
                        VectorXd b = VectorXd::Random(n);
                      
                        VectorXd x = VectorXd::Zero(n);
                        VectorXd y = VectorXd::Zero(n);
                      
                        // Uncomment to reduce the condition number
                        // R += 10*MatrixXd::Identity(n,n);
                      
                        y = R.triangularView<Eigen::Upper>().solve(b);
                        x << R.triangularView<Eigen::Upper>().solve(b);
                      
                        std::cout << "res(x): " << (b - R.triangularView<Eigen::Upper>() * x).norm() << std::endl;
                        std::cout << "res(y): " << (b - R.triangularView<Eigen::Upper>() * y).norm() << std::endl;
                      
                        Eigen::BDCSVD<Eigen::MatrixXd> svd(R.triangularView<Eigen::Upper>());
                        VectorXd sigma = svd.singularValues();
                        std::cout << "cond: " << sigma.maxCoeff() / sigma.minCoeff() << std::endl;
                      
                        std::cout << "error norm: " << (x-y).norm() << std::endl;
                        std::cout << "error max: " << (x-y).lpNorm<Eigen::Infinity>() << std::endl;
                      
                        return 0;
                      }
                      
                       x << VectorXd(R.triangularView<Eigen::Upper>().solve(b));
                      
                      y = R.triangularView<Eigen::Upper>().solve(b);
                      x << R.triangularView<Eigen::Upper>().solve(b);
                      
                      R += 10*MatrixXd::Identity(n,n);
                      
                      #include <iostream>
                      #include <Eigen/Dense>
                      #include <Eigen/SVD>
                      
                      using Eigen::MatrixXd;
                      using Eigen::VectorXd;
                      using Eigen::BDCSVD;
                      
                      int main()
                      {
                        const unsigned int n = 25;
                        MatrixXd R = MatrixXd::Random(n,n);
                        VectorXd b = VectorXd::Random(n);
                      
                        VectorXd x = VectorXd::Zero(n);
                        VectorXd y = VectorXd::Zero(n);
                      
                        // Uncomment to reduce the condition number
                        // R += 10*MatrixXd::Identity(n,n);
                      
                        y = R.triangularView<Eigen::Upper>().solve(b);
                        x << R.triangularView<Eigen::Upper>().solve(b);
                      
                        std::cout << "res(x): " << (b - R.triangularView<Eigen::Upper>() * x).norm() << std::endl;
                        std::cout << "res(y): " << (b - R.triangularView<Eigen::Upper>() * y).norm() << std::endl;
                      
                        Eigen::BDCSVD<Eigen::MatrixXd> svd(R.triangularView<Eigen::Upper>());
                        VectorXd sigma = svd.singularValues();
                        std::cout << "cond: " << sigma.maxCoeff() / sigma.minCoeff() << std::endl;
                      
                        std::cout << "error norm: " << (x-y).norm() << std::endl;
                        std::cout << "error max: " << (x-y).lpNorm<Eigen::Infinity>() << std::endl;
                      
                        return 0;
                      }
                      
                       x << VectorXd(R.triangularView<Eigen::Upper>().solve(b));
                      
                      const unsigned int n = 25;
                      Eigen::MatrixXd R = Eigen::MatrixXd::Random(n,n);
                      Eigen::VectorXd b = Eigen::VectorXd::Random(n);
                      
                      // Eigen::VectorXd x = Eigen::VectorXd::Zero(n);
                      Eigen::VectorXd y = Eigen::VectorXd::Zero(n);
                      
                      y = R.triangularView<Eigen::Upper>().solve(b);
                      auto x = R.triangularView<Eigen::Upper>().solve(b); // x is an Eigen::Solve
                      
                      // assert((x-y).norm() < std::numeric_limits<double>::epsilon()*10E6);
                      std::cout << (x-y).norm() << std::endl; // will print 0
                      
                      Eigen::VectorXd xa = R.triangularView<Eigen::Upper>().solve(b);
                      Eigen::MatrixXd xb = R.triangularView<Eigen::Upper>().solve(b);
                      

                      Can I trust the results from `nloptr` in R?

                      copy iconCopydownload iconDownload
                      loss = function(W, digits = 20){
                        return(round(sqrt(mean(y - X %*% W)^2)),digits)
                      }
                      
                      > m$value
                      [1] 2.314992
                      > round(m$par,3)
                       [1] 0.000 0.000 0.000 0.085 0.000 0.000 0.000 0.000
                       [9] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
                      [17] 0.000 0.000 0.000 0.113 0.105 0.457 0.000 0.000
                      [25] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
                      [33] 0.240 0.000 0.000 0.000 0.000 0.000
                      
                      loss = function(W, digits = 20){
                        return(round(sqrt(mean(y - X %*% W)^2)),digits)
                      }
                      
                      > m$value
                      [1] 2.314992
                      > round(m$par,3)
                       [1] 0.000 0.000 0.000 0.085 0.000 0.000 0.000 0.000
                       [9] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
                      [17] 0.000 0.000 0.000 0.113 0.105 0.457 0.000 0.000
                      [25] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
                      [33] 0.240 0.000 0.000 0.000 0.000 0.000
                      

                      Problem with row-wise operation in base R

                      copy iconCopydownload iconDownload
                      sapply(seq_len(nrow(start)), function(i) d(start[i,], stop[i,]))
                      [1] 12.165525 20.000000 16.031220  1.414214
                      
                      apply(cbind(start, stop), 1, FUN = function(x) d(x[1:2], x[3:4]))
                      [1] 12.165525 20.000000 16.031220  1.414214
                      
                      library(collapse)
                      dapply(cbind(start, stop), MARGIN = 1, parallel = TRUE,
                         FUN = function(x) d(x[1:2], x[3:4]))
                      [1] 12.165525 20.000000 16.031220  1.414214
                      
                      sapply(seq_len(nrow(start)), function(i) d(start[i,], stop[i,]))
                      [1] 12.165525 20.000000 16.031220  1.414214
                      
                      apply(cbind(start, stop), 1, FUN = function(x) d(x[1:2], x[3:4]))
                      [1] 12.165525 20.000000 16.031220  1.414214
                      
                      library(collapse)
                      dapply(cbind(start, stop), MARGIN = 1, parallel = TRUE,
                         FUN = function(x) d(x[1:2], x[3:4]))
                      [1] 12.165525 20.000000 16.031220  1.414214
                      
                      sapply(seq_len(nrow(start)), function(i) d(start[i,], stop[i,]))
                      [1] 12.165525 20.000000 16.031220  1.414214
                      
                      apply(cbind(start, stop), 1, FUN = function(x) d(x[1:2], x[3:4]))
                      [1] 12.165525 20.000000 16.031220  1.414214
                      
                      library(collapse)
                      dapply(cbind(start, stop), MARGIN = 1, parallel = TRUE,
                         FUN = function(x) d(x[1:2], x[3:4]))
                      [1] 12.165525 20.000000 16.031220  1.414214
                      

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                      QUESTION

                      Does `table` round numeric values?

                      Asked 2022-Mar-14 at 20:13

                      I have 120 vectors in a matrix points (120 x 2). I calculate their squared norms:

                      norms2 <- apply(points, 1L, crossprod)
                      

                      I tabulate these squared norms:

                      > table(norms2)
                      norms2
                      0.410691055416468  1.62481505182984  2.37518494817016  3.58930894458353 
                                     30                30                30                30 
                      

                      One sees that the squared norms take four possible values, 30 vectors for each value.

                      I extract the vectors which have the smallest squared norm:

                      > points[norms2 == min(norms2), ]
                                  [,1]       [,2]
                      [1,]  0.06698726 -0.6373412
                      [2,]  0.06698726  0.6373412
                      [3,] -0.06698726 -0.6373412
                      [4,] -0.06698726  0.6373412
                      

                      Why do I get four vectors only, and not 30?

                      If I extract with an approximate equality, I get 30 vectors:

                      > dim(points[abs(norms2 - min(norms2)) < 0.001, ])
                      [1] 30  2
                      

                      So what is the explanation? Does table round the values?

                      ANSWER

                      Answered 2022-Mar-14 at 20:13

                      Yes, table can round numeric input.

                      table() calls factor() which calls as.character(), and as.character() does some rounding:

                      x = sqrt(2)
                      print(x, digits = 22)
                      # [1] 1.414213562373095145475
                      as.character(x)
                      # [1] "1.4142135623731"
                      

                      Here's a reproducible example:

                      x = c(pi, pi + 1e-15)
                      
                      x == pi
                      # [1]  TRUE FALSE
                      
                      as.character(x)
                      # [1] "3.14159265358979" "3.14159265358979"
                      
                      table(x)
                      # x
                      # 3.14159265358979 
                      #                2 
                      

                      Source https://stackoverflow.com/questions/71473117

                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                      Vulnerabilities

                      No vulnerabilities reported

                      Install norm

                      You can download it from GitHub, Maven.
                      You can use norm like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the norm component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                      Support

                      For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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