Python plot_k_meansの例

コード例 #1

ファイル: choosek.py プロジェクト: pashu123/Unsupervised

def main():
    X = get_simple_data()

    costs = np.empty(10)
    costs[0] = None

    for k in range(1, 10):
        c = plot_k_means(X, k)
        costs[k] = c

    plt.plot(costs)
    plt.title('cost vs K')
    plt.show()

コード例 #2

def main():
    X = create_sample_data()

    plt.scatter(X[:, 0], X[:, 1])
    plt.show()

    costs = np.empty(10)
    costs[0] = None
    for k in range(1, 10):
        M, R = plot_k_means(X, k, show_plots=False)
        costs[k] = cost(X, R, M)

    plt.plot(costs)
    plt.title("Cost vs K")
    plt.show()

コード例 #3

ファイル: choose_k.py プロジェクト: cmagnusb/machine_learning_examples

def main():
  X = get_simple_data()

  plt.scatter(X[:,0], X[:,1])
  plt.show()

  costs = np.empty(10)
  costs[0] = None
  for k in range(1, 10):
    M, R = plot_k_means(X, k, show_plots=False)
    c = cost(X, R, M)
    costs[k] = c

  plt.plot(costs)
  plt.title("Cost vs K")
  plt.show()

コード例 #4

ファイル: kmeans_mnist.py プロジェクト: ajanaliz/Usupervised-ML-Theano

def main():
    X, Y = get_data(1000)

    # simple data
    # X = get_simple_data()
    # Y = np.array([0]*300 + [1]*300 + [2]*300)

    print("Number of data points:", len(Y))
    # Note: I modified plot_k_means from the original
    # lecture to return means and responsibilities
    # print "performing k-means..."
    # t0 = datetime.now()
    M, R = plot_k_means(X, len(set(Y)))
    # print "k-means elapsed time:", (datetime.now() - t0)
    # Exercise: Try different values of K and compare the evaluation metrics
    print("Purity:", purity(Y, R))
    print("DBI:", DBI(X, M, R))

コード例 #5

ファイル: kmeans_mnist.py プロジェクト: renjinghai/machine_learning_examples

def main():
    X, Y = get_data(1000)

    # simple data
    # X = get_simple_data()
    # Y = np.array([0]*300 + [1]*300 + [2]*300)

    print "Number of data points:", len(Y)
    # Note: I modified plot_k_means from the original
    # lecture to return means and responsibilities
    # print "performing k-means..."
    # t0 = datetime.now()
    M, R = plot_k_means(X, len(set(Y)))
    # print "k-means elapsed time:", (datetime.now() - t0)
    # Exercise: Try different values of K and compare the evaluation metrics
    print "Purity:", purity(Y, R)
    print "DBI:", DBI(X, M, R)

コード例 #6

def main():
    X = donut()
    plot_k_means(X, 2)

    X = np.zeros((1000, 2))
    X[:500, :] = np.random.multivariate_normal([0, 0], [[1, 0], [0, 20]], 500)
    X[500:, :] = np.random.multivariate_normal([5, 0], [[1, 0], [0, 20]], 500)
    plot_k_means(X, 2)

    X = np.zeros((1000, 2))
    X[:950, :] = np.array([0, 0]) + np.random.randn(950, 2)
    X[950:, :] = np.array([3, 0]) + np.random.randn(50, 2)
    plot_k_means(X, 2)

コード例 #7

ファイル: kmeans_fail.py プロジェクト: hmanjarawala/Python

def main():
    X = donut()
    plot_k_means(X, 2, beta=0.1, show_plots=True)

    # elongated clusters
    X = np.zeros((1000, 2))
    X[:500, :] = np.random.multivariate_normal([0, 0], [[1, 0], [0, 20]], 500)
    X[500:, :] = np.random.multivariate_normal([5, 0], [[1, 0], [0, 20]], 500)
    plot_k_means(X, 2, beta=0.1, show_plots=True)

    # different density
    X = np.zeros((1000, 2))
    X[:950, :] = np.array([0, 0]) + np.random.randn(950, 2)
    X[950:, :] = np.array([3, 0]) + np.random.randn(50, 2)
    plot_k_means(X, 2, show_plots=True)

コード例 #8

ファイル: kmeans_mnist.py プロジェクト: hmanjarawala/Python

def main():
    X, Y = get_data(1000)

    #    # sample data
    #    X = create_sample_data()
    #    Y = np.array([0]*300 + [1]*300 + [2]*300)

    print("Number of data points:", len(Y))
    M, R = plot_k_means(X, len(set(Y)))

    print("Purity:", purity(Y, R))
    print("Purity 2 (hard clusters):", purity2(Y, R))
    print("DBI:", DBI(X, M, R))
    print("DBI 2 (hard clusters):", DBI2(X, R))

    # plot the mean images
    # they should look like digits
    for k in range(len(M)):
        im = M[k].reshape(28, 28)
        plt.imshow(im, cmap='gray')
        plt.show()

コード例 #9

ファイル: kmeans_fail.py プロジェクト: cmagnusb/machine_learning_examples

def main():
    # donut
    X = donut()
    plot_k_means(X, 2)

    # elongated clusters
    X = np.zeros((1000, 2))
    X[:500,:] = np.random.multivariate_normal([0, 0], [[1, 0], [0, 20]], 500)
    X[500:,:] = np.random.multivariate_normal([5, 0], [[1, 0], [0, 20]], 500)
    plot_k_means(X, 2)

    # different density
    X = np.zeros((1000, 2))
    X[:950,:] = np.array([0,0]) + np.random.randn(950, 2)
    X[950:,:] = np.array([3,0]) + np.random.randn(50, 2)
    plot_k_means(X, 2)

コード例 #10

def main():
    # mnist data
    X, Y = get_data(10000)

    # simple data
    # X = get_simple_data()
    # Y = np.array([0]*300 + [1]*300 + [2]*300)

    print("Number of data points:", len(Y))
    M, R = plot_k_means(X, len(set(Y)))
    # Exercise: Try different values of K and compare the evaluation metrics
    print("Purity:", purity(Y, R))
    print("Purity 2 (hard clusters):", purity2(Y, R))
    print("DBI:", DBI(X, M, R))
    print("DBI 2 (hard clusters):", DBI2(X, R))

    # plot the mean images
    # they should look like digits
    for k in range(len(M)):
        im = M[k].reshape(28, 28)
        plt.imshow(im, cmap='gray')
        plt.show()

コード例 #11

ファイル: kmeans_mnist.py プロジェクト: ShuvenduBikash/machine_learning_examples

def main():
    # mnist data
    X, Y = get_data(10000)

    # simple data
    # X = get_simple_data()
    # Y = np.array([0]*300 + [1]*300 + [2]*300)

    print "Number of data points:", len(Y)
    M, R = plot_k_means(X, len(set(Y)))
    # Exercise: Try different values of K and compare the evaluation metrics
    print "Purity:", purity(Y, R)
    print "Purity 2 (hard clusters):", purity2(Y, R)
    print "DBI:", DBI(X, M, R)
    print "DBI 2 (hard clusters):", DBI2(X, R)

    # plot the mean images
    # they should look like digits
    for k in xrange(len(M)):
        im = M[k].reshape(28, 28)
        plt.imshow(im, cmap='gray')
        plt.show()

コード例 #12

def main():

    ## Donut problem
    X = donut()
    plot_k_means(X, 2)

    ## Two class elongated gaussians
    X = np.zeros((1000, 2))
    X[:500, :] = np.random.multivariate_normal([0, 0], [[1, 0], [0, 20]], 500)
    X[500:, :] = np.random.multivariate_normal([5, 0], [[1, 0], [0, 20]], 500)
    plot_k_means(X, 2)

    ## Denisty wise gaussian
    X = np.zeros((1000, 2))
    X[:950, :] = np.array([0, 0]) + np.random.randn(950, 2)
    X[950:, :] = np.array([3, 0]) + np.random.randn(50, 2)
    plot_k_means(X, 2)

コード例 #13

ファイル: kmeans_mnist.py プロジェクト: jerryxyx/Python

def main():
    X, Y = get_data(10000)

    # simple data
    # X = get_simple_data()
    # Y = np.array([0]*300 + [1]*300 + [2]*300)

    print "Number of data points:", len(Y)
    # Note: I modified plot_k_means from the original
    # lecture to return means and responsibilities
    # print "performing k-means..."
    # t0 = datetime.now()
    M, R = plot_k_means(X, len(set(Y)))
    # print "k-means elapsed time:", (datetime.now() - t0)
    # Exercise: Try different values of K and compare the evaluation metrics
    print "Purity:", purity(Y, R)
    print "DBI:", DBI(X, M, R)

    # plot the mean images
    # they should look like digits
    for k in xrange(len(M)):
        im = M[k].reshape(28, 28)
        plt.imshow(im, cmap='gray')
        plt.show()

コード例 #14

ファイル: kmeans_mnist.py プロジェクト: vivianduan/machine_learning_examples

def main():
    X, Y = get_data(10000)

    # simple data
    # X = get_simple_data()
    # Y = np.array([0]*300 + [1]*300 + [2]*300)

    print "Number of data points:", len(Y)
    # Note: I modified plot_k_means from the original
    # lecture to return means and responsibilities
    # print "performing k-means..."
    # t0 = datetime.now()
    M, R = plot_k_means(X, len(set(Y)))
    # print "k-means elapsed time:", (datetime.now() - t0)
    # Exercise: Try different values of K and compare the evaluation metrics
    print "Purity:", purity(Y, R)
    print "DBI:", DBI(X, M, R)

    # plot the mean images
    # they should look like digits
    for k in xrange(len(M)):
        im = M[k].reshape(28, 28)
        plt.imshow(im, cmap='gray')
        plt.show()

コード例 #15

ファイル: choose_k.py プロジェクト: OscarAbacka/machine_learning_examples

>>>>>>> upstream/master
import numpy as np
import matplotlib.pyplot as plt
from kmeans import plot_k_means, get_simple_data, cost


def main():
  X = get_simple_data()

  plt.scatter(X[:,0], X[:,1])
  plt.show()

  costs = np.empty(10)
  costs[0] = None
<<<<<<< HEAD
  for k in xrange(1, 10):
=======
  for k in range(1, 10):
>>>>>>> upstream/master
    M, R = plot_k_means(X, k, show_plots=False)
    c = cost(X, R, M)
    costs[k] = c

  plt.plot(costs)
  plt.title("Cost vs K")
  plt.show()


if __name__ == '__main__':
  main()

コード例 #16

ファイル: kmeans_mnist.py プロジェクト: OscarAbacka/machine_learning_examples

=======
    # mnist data
>>>>>>> upstream/master
    X, Y = get_data(10000)

    # simple data
    # X = get_simple_data()
    # Y = np.array([0]*300 + [1]*300 + [2]*300)

<<<<<<< HEAD
    print "Number of data points:", len(Y)
    # Note: I modified plot_k_means from the original
    # lecture to return means and responsibilities
    # print "performing k-means..."
    # t0 = datetime.now()
    M, R = plot_k_means(X, len(set(Y)))
    # print "k-means elapsed time:", (datetime.now() - t0)
    # Exercise: Try different values of K and compare the evaluation metrics
    print "Purity:", purity(Y, R)
    print "DBI:", DBI(X, M, R)

    # plot the mean images
    # they should look like digits
    for k in xrange(len(M)):
=======
    print("Number of data points:", len(Y))
    M, R = plot_k_means(X, len(set(Y)))
    # Exercise: Try different values of K and compare the evaluation metrics
    print("Purity:", purity(Y, R))
    print("Purity 2 (hard clusters):", purity2(Y, R))
    print("DBI:", DBI(X, M, R))