Ejemplos de purity en Python

Ejemplo n.º 1

Archivo: xwing.py Proyecto: marcovarrub/python

def main():
    Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()
    dae = DeepAutoEncoder([500, 300, 2])
    dae.fit(Xtrain)
    mapping = dae.map2center(Xtrain)
    plt.scatter(mapping[:, 0], mapping[:, 1], c=Ytrain, s=100, alpha=0.5)
    plt.show()

    # purity measure from unsupervised machine learning pt 1
    _, Rfull = gmm(X, 10, max_iter=30)
    print "full purity:", purity(Y, Rfull)
    _, Rreduced = plot_k_means(Z, 10, max_iter=30)
    print "reduced purity:", purity(Y, Rreduced)

Ejemplo n.º 2

def main():
    Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()
    dae = DeepAutoEncoder([500, 300, 2])
    dae.fit(Xtrain)
    mapping = dae.map2center(Xtrain)
    plt.scatter(mapping[:, 0], mapping[:, 1], c=Ytrain, s=100, alpha=0.5)
    plt.show()

    # purity measure from unsupervised machine learning pt 1
    gmm = GaussianMixture(n_components=10)
    gmm.fit(Xtrain)
    responsibilities_full = gmm.predict_proba(Xtrain)
    print "full purity:", purity(Ytrain, responsibilities_full)

    gmm.fit(mapping)
    responsibilities_reduced = gmm.predict_proba(mapping)
    print "reduced purity:", purity(Ytrain, responsibilities_reduced)

Ejemplo n.º 3

Archivo: tsne_mnist.py Proyecto: jerryxyx/Python

def main():
    Xtrain, Ytrain, _, _ = getKaggleMNIST()

    sample_size = 1000
    X = Xtrain[:sample_size]
    Y = Ytrain[:sample_size]

    tsne = TSNE()
    Z = tsne.fit_transform(X)
    plt.scatter(Z[:, 0], Z[:, 1], s=100, c=Y, alpha=0.5)
    plt.show()

    # purity measure from unsupervised machine learning pt 1
    _, Rfull = gmm(X, 10, max_iter=30, smoothing=10e-1)
    print "full purity:", purity(Y, Rfull)
    _, Rreduced = gmm(Z, 10, max_iter=30, smoothing=10e-1)
    print "reduced purity:", purity(Y, Rreduced)

Ejemplo n.º 4

Archivo: xwing.py Proyecto: ShuvenduBikash/machine_learning_examples

def main():
    Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()
    dae = DeepAutoEncoder([500, 300, 2])
    dae.fit(Xtrain)
    mapping = dae.map2center(Xtrain)
    plt.scatter(mapping[:,0], mapping[:,1], c=Ytrain, s=100, alpha=0.5)
    plt.show()

    # purity measure from unsupervised machine learning pt 1
    gmm = GaussianMixture(n_components=10)
    gmm.fit(Xtrain)
    responsibilities_full = gmm.predict_proba(Xtrain)
    print "full purity:", purity(Ytrain, responsibilities_full)

    gmm.fit(mapping)
    responsibilities_reduced = gmm.predict_proba(mapping)
    print "reduced purity:", purity(Ytrain, responsibilities_reduced)

Ejemplo n.º 5

Archivo: tsne_mnist.py Proyecto: ShuvenduBikash/machine_learning_examples

def main():
    Xtrain, Ytrain, _, _ = getKaggleMNIST()

    sample_size = 1000
    X = Xtrain[:sample_size]
    Y = Ytrain[:sample_size]

    tsne = TSNE()
    Z = tsne.fit_transform(X)
    plt.scatter(Z[:,0], Z[:,1], s=100, c=Y, alpha=0.5)
    plt.show()

    # purity measure from unsupervised machine learning pt 1
    _, Rfull = gmm(X, 10, max_iter=30, smoothing=10e-1)
    print "full purity:", purity(Y, Rfull)
    _, Rreduced = gmm(Z, 10, max_iter=30, smoothing=10e-1)
    print "reduced purity:", purity(Y, Rreduced)

Ejemplo n.º 6

Archivo: autoencoder_xwing.py Proyecto: CamomilaMorais/Deep-Learning

def main():
    Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()
    dae = DeepAutoEncoder([500, 300, 2])
    dae.fit(Xtrain)
    mapping = dae.map2center(Xtrain)
    plt.scatter(mapping[:, 0], mapping[:, 1], c=Ytrain, s=100, alpha=0.5)
    plt.show()

    # purity measure from unsupervised machine learning pt 1
    # NOTE: this will take a long time (i.e. just leave it overnight)
    gmm = GaussianMixture(n_components=10)
    gmm.fit(Xtrain)
    print("Finished GMM training")
    responsibilities_full = gmm.predict_proba(Xtrain)
    print("full purity:", purity(Ytrain, responsibilities_full))

    gmm.fit(mapping)
    responsibilities_reduced = gmm.predict_proba(mapping)
    print("reduced purity:", purity(Ytrain, responsibilities_reduced))

Ejemplo n.º 7

Archivo: xwing.py Proyecto: cmagnusb/machine_learning_examples

def main():
    Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()
    dae = DeepAutoEncoder([500, 300, 2])
    dae.fit(Xtrain)
    mapping = dae.map2center(Xtrain)
    plt.scatter(mapping[:,0], mapping[:,1], c=Ytrain, s=100, alpha=0.5)
    plt.show()

    # purity measure from unsupervised machine learning pt 1
    # NOTE: this will take a long time (i.e. just leave it overnight)
    gmm = GaussianMixture(n_components=10)
    gmm.fit(Xtrain)
    print("Finished GMM training")
    responsibilities_full = gmm.predict_proba(Xtrain)
    print("full purity:", purity(Ytrain, responsibilities_full))

    gmm.fit(mapping)
    responsibilities_reduced = gmm.predict_proba(mapping)
    print("reduced purity:", purity(Ytrain, responsibilities_reduced))

Ejemplo n.º 8

Archivo: tsne_mnist.py Proyecto: Sebster10/Pandas-Practice

def main():
    Xtrain, Ytrain, _, _ = getKaggleMNIST()

    sample_size = 1000
    X = Xtrain[:sample_size]
    Y = Ytrain[:sample_size]

    tsne = TSNE()
    Z = tsne.fit_transform(X)
    plt.scatter(Z[:, 0], Z[:, 1], s=100, c=Y, alpha=0.5)
    plt.show()

    # purity measure from unsupervised machine learning pt 1
    # maximum purity is 1, higher is better
    gmm = GaussianMixture(n_components=10)
    gmm.fit(X)
    Rfull = gmm.predict_proba(X)
    print("Rfull.shape:", Rfull.shape)
    print("full purity:", purity(Y, Rfull))

    # now try the same thing on the reduced data
    gmm.fit(Z)
    Rreduced = gmm.predict_proba(Z)
    print("reduced purity:", purity(Y, Rreduced))

Ejemplo n.º 9

Archivo: xwing.py Proyecto: OscarAbacka/machine_learning_examples

def main():
    Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()
    dae = DeepAutoEncoder([500, 300, 2])
    dae.fit(Xtrain)
    mapping = dae.map2center(Xtrain)
    plt.scatter(mapping[:,0], mapping[:,1], c=Ytrain, s=100, alpha=0.5)
    plt.show()

    # purity measure from unsupervised machine learning pt 1
<<<<<<< HEAD
    gmm = GaussianMixture(n_components=10)
    gmm.fit(Xtrain)
    responsibilities_full = gmm.predict_proba(Xtrain)
    print "full purity:", purity(Ytrain, responsibilities_full)

    gmm.fit(mapping)
    responsibilities_reduced = gmm.predict_proba(mapping)
    print "reduced purity:", purity(Ytrain, responsibilities_reduced)
=======
    # NOTE: this will take a long time (i.e. just leave it overnight)
    gmm = GaussianMixture(n_components=10)
    gmm.fit(Xtrain)
    print("Finished GMM training")
    responsibilities_full = gmm.predict_proba(Xtrain)
    print("full purity:", purity(Ytrain, responsibilities_full))

    gmm.fit(mapping)
    responsibilities_reduced = gmm.predict_proba(mapping)
    print("reduced purity:", purity(Ytrain, responsibilities_reduced))

Ejemplo n.º 10

def main():
    Xtrain, Ytrain, _, _ = getKaggleMNIST()

    sample_size = 1000
    X = Xtrain[:sample_size]
    Y = Ytrain[:sample_size]

    tsne = TSNE()
    Z = tsne.fit_transform(X)
    plt.scatter(Z[:,0], Z[:,1], s=100, c=Y, alpha=0.5)
    plt.show()

    # purity measure from unsupervised machine learning pt 1
<<<<<<< HEAD
    _, Rfull = gmm(X, 10, max_iter=30, smoothing=10e-1)
    print "full purity:", purity(Y, Rfull)
    _, Rreduced = gmm(Z, 10, max_iter=30, smoothing=10e-1)
    print "reduced purity:", purity(Y, Rreduced)
=======
    # maximum purity is 1, higher is better
    gmm = GaussianMixture(n_components=10)
    gmm.fit(X)
    Rfull = gmm.predict_proba(X)
    print("Rfull.shape:", Rfull.shape)
    print("full purity:", purity(Y, Rfull))

    # now try the same thing on the reduced data
    gmm.fit(Z)
    Rreduced = gmm.predict_proba(Z)
    print("reduced purity:", purity(Y, Rreduced))
>>>>>>> upstream/master