コード例 #1
0
def cross_validation(method, k=5):
    if method == "lda":
        Classifier = LDAClassifier
    elif method == "qda":
        Classifier = QDAClassifier
    else:
        raise Exception("lda or qda only")

    timer.start("folding data into", k, "copies")
    data_slice = [None] * k
    labl_slice = [None] * k
    train_rate = [0.0] * k
    valid_rate = [0.0] * k
    n = len(labl)
    m = n / k
    for i in range(k):
        data_slice[i] = data[(i * m):min((i + 1) * m, n)]
        labl_slice[i] = labl[(i * m):min((i + 1) * m, n)]
    timer.end("done")

    for j in range(k):
        timer.start("validation iteration #", j)
        training_data = np.concatenate(
            tuple(data_slice[i] for i in range(k) if i != j))
        training_labl = np.concatenate(
            tuple(labl_slice[i] for i in range(k) if i != j))
        print ".... data formating done"
        c = LDAClassifier(training_data, training_labl)
        print ".... classifier training done"
        train_rate[j] = c.score(c.classify_all(training_data), training_labl)
        print ".... training accuracy computation done"
        valid_rate[j] = c.score(c.classify_all(data_slice[j]), labl_slice[j])
        print ".... validation accuracy computation done"
        timer.end("done; training accuracy =", train_rate[j],
                  "; validation accuracy =", valid_rate[j])

    print k, "fold cross validation for", method, "on dataset", which, "complete"
    print ".... overall training accuracy   =", np.mean(train_rate)
    print ".... overall validation accuracy =", np.mean(valid_rate)
コード例 #2
0
ファイル: 0029.py プロジェクト: CrumbleZ/project-euler
"""

Problem :
    How many distinct terms are in the sequence generated by a^b for 2 ≤ a ≤ 100
    and 2 ≤ b ≤ 100?

Performance time: ~0.0059s

"""

from timer import timer

timer.start()
print(len(set(a**b for a in range(2, 101) for b in range(2, 101))))
timer.stop()
コード例 #3
0
ファイル: 0065.py プロジェクト: CrumbleZ/project-euler
"""

Problem:
    Find the sum of digits in the numerator of the 100th convergent of the
    continued fraction for e.

Performance time: ~0.0012s

"""

from fractions import Fraction
from timer import timer
from utils import sum_of_digits


timer.start()

a = [int(n / 3 * 2) if n % 3 == 0 else 1 for n in range(1, 101)]
a[0] = 2

def convergent_e(limit=-1, index=0):
    if limit == 0:
        return a[0]
    elif index == limit - 1:
        return a[index] + Fraction(1, a[index+1])
    else:
        return a[index] + Fraction(1, convergent_e(limit, index+1))

print(sum_of_digits(Fraction(convergent_e(99)).numerator))

timer.stop()
コード例 #4
0
import numpy as np
from os import path
from scipy.io import loadmat
from timer import timer
from classifier import LDAClassifier, QDAClassifier
""" TODO: choose either mnist or spam >>HERE<< """
which = "spam.mat"
which = "mnist.mat"
""" TODO: choose either mnist or spam >>HERE<< """

timer.start("reading", which, "data from matlab file")
raw = loadmat(path.join(path.dirname(__file__), "data", which))
raw_data = raw['data']
raw_labl = raw['label'][0]
timer.end("done")

timer.start("permuting data randomly")
np.random.seed(0)
ordering = np.random.permutation(len(raw_data))
data = np.ndarray(shape=raw_data.shape, dtype=raw_data.dtype)
labl = np.ndarray(shape=raw_labl.shape, dtype=raw_labl.dtype)
for old, new in enumerate(ordering):
    data[new] = raw_data[old]
    labl[new] = raw_labl[old]
del raw, raw_data, raw_labl, ordering
timer.end("done")


def cross_validation(method, k=5):
    if method == "lda":
        Classifier = LDAClassifier