Python get_binary_data Beispiele

Beispiel #1

import numpy as np
from process import get_binary_data

X, Y = get_binary_data()  # data after one hot encoding

D = X.shape[1]  # D is the no of features

w = np.random.randn(D)  # w is a cloumn vector

b = 0


def sigmoid(a):
    return 1 / (1 + np.exp(-a))


def forward(X, w, b):
    return sigmoid(X.dot(w) + b)


P_Y_given_X = forward(X, w, b)
print("P_Y_given_X=\n"), P_Y_given_X[1:10]
predictions = np.round(P_Y_given_X)
print("predictions=\n"), predictions[1:10]


def classification_rate(Y, P):
    return np.mean(Y == P)


print "Score", classification_rate(Y, predictions)

Beispiel #2

Datei: adp_logistic_train.py Projekt: adparker/machine_learning_examples

import numpy as np
import matplotlib.pyplot as plt

from sklearn.utils import shuffle
from process import get_binary_data

Xtrain, Ytrain, Xtest, Ytest = get_binary_data()
#X, Y = shuffle(X, Y)

# Separate out test and training data.
# testsize = 100
# Xtrain = X[:-testsize]
# Ytrain = Y[:-testsize]
# Xtest = X[-testsize:]
# Ytest = Y[-testsize:]

# Randomly initialize weights
D = Xtrain.shape[1]
W = np.random.randn(D)
b = 0


def sigmoid(z):
    return 1 / (1 + np.exp(-z))


def forward(X, W, b):
    return sigmoid(X.dot(W) + b)


def classification_rate(Y, P):

Beispiel #3

import numpy as np
from process import get_binary_data

X, y = get_binary_data()

D = X.shape[1]
W = np.random.randn(D)
b = 0


def sigmoid(a):
    return 1 / (1 + np.exp(-a))


def forward(X, W, b):
    return sigmoid(X.dot(W) + b)


P_y_given_X = forward(X, W, b)
predictions = np.round(P_y_given_X)


def classification_rate(Y, P):
    return np.mean(Y == P)


print("Score:", classification_rate(y, predictions))

Beispiel #4

Datei: mylogistic_pred.py Projekt: RKorzeniowski/Lazy_programmer_projects

import numpy as np
from process import get_binary_data

X, Y = get_binary_data()

D = X.shape[1]
W = np.random.randn(D)
b = 0


def sigmoid(a):
    return 1 / (1 + np.exp(-a))


def forward(X, W, b):
    return sigmoid(X.dot(W) + b)


P_Y_given_X = forward(X, W, b)
predictions = np.round(P_Y_given_X)


def classificaiton_rate(Y, P):
    return np.mean(Y == P)


print("Score ", classificaiton_rate(Y, predictions))

Beispiel #5

from __future__ import print_function, division
# Note: you may need to update your version of future
# sudo pip install -U future

import numpy as np
from process import get_binary_data

X, Y, _, _ = get_binary_data()

print(X)
print(X.shape)

print(Y)
print(Y.shape)

# randomly initialize weights
D = X.shape[1]
W = np.random.randn(D)
b = 0  # bias term

# make predictions
# sigmoid takes a function in form mx + b


def sigmoid(a):
    return 1 / (1 + np.exp(-a))


def forward(X, W, b):
    return sigmoid(X.dot(W) + b)

Beispiel #6

Datei: logistic_predict.py Projekt: AndreyDrv/machine_learning_examples

import numpy as np
from process import get_binary_data

X, Y = get_binary_data()

# randomly initialize weights
D = X.shape[1]
W = np.random.randn(D)
b = 0 # bias term

# make predictions
def sigmoid(a):
    return 1 / (1 + np.exp(-a))

def forward(X, W, b):
    return sigmoid(X.dot(W) + b)

P_Y_given_X = forward(X, W, b)
predictions = np.round(P_Y_given_X)

# calculate the accuracy
def classification_rate(Y, P):
    return np.mean(Y == P)

print "Score:", classification_rate(Y, predictions)

Beispiel #7

Datei: logistic_predict.py Projekt: cmagnusb/machine_learning_examples

from __future__ import print_function, division
from builtins import range
# Note: you may need to update your version of future
# sudo pip install -U future


import numpy as np
from process import get_binary_data

X, Y, _, _ = get_binary_data()

# randomly initialize weights
D = X.shape[1]
W = np.random.randn(D)
b = 0 # bias term

# make predictions
def sigmoid(a):
    return 1 / (1 + np.exp(-a))

def forward(X, W, b):
    return sigmoid(X.dot(W) + b)

P_Y_given_X = forward(X, W, b)
predictions = np.round(P_Y_given_X)

# calculate the accuracy
def classification_rate(Y, P):
    return np.mean(Y == P)

print("Score:", classification_rate(Y, predictions))

Beispiel #8

Datei: trainingData.py Projekt: amelnychuk/LogisticRegression

 def __init__(self):
     self.X = DataPair()
     self.Y = DataPair()
     self.X.train, self.Y.train, self.X.test, self.Y.test = get_binary_data(
     )

Beispiel #9

Datei: logistic_train.py Projekt: cmagnusb/machine_learning_examples

from __future__ import print_function, division
from builtins import range
# Note: you may need to update your version of future
# sudo pip install -U future


import numpy as np
import matplotlib.pyplot as plt

from sklearn.utils import shuffle
from process import get_binary_data

# get the data
Xtrain, Ytrain, Xtest, Ytest = get_binary_data()

# randomly initialize weights
D = Xtrain.shape[1]
W = np.random.randn(D)
b = 0 # bias term

# make predictions
def sigmoid(a):
    return 1 / (1 + np.exp(-a))

def forward(X, W, b):
    return sigmoid(X.dot(W) + b)

# calculate the accuracy
def classification_rate(Y, P):
    return np.mean(Y == P)