Python purity Exemples

Exemple #1

Fichier : xwing.py Projet : 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)

Exemple #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)

Exemple #3

Fichier : tsne_mnist.py Projet : 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)

Exemple #4

Fichier : xwing.py Projet : 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)

Exemple #5

Fichier : tsne_mnist.py Projet : 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)

Exemple #6

Fichier : autoencoder_xwing.py Projet : 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))

Exemple #7

Fichier : xwing.py Projet : 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))

Exemple #8

Fichier : tsne_mnist.py Projet : 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))

Exemple #9

Fichier : xwing.py Projet : 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))

Exemple #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