def sampleTraining(X, Y, Xt, Yt=None):
# Function used for testing various model of training
# Class weight
class_weight = {0:0.6, 1:0.4}
linearRegression(X, Y, Xt, Yt)
logisticRegression(X, Y, Xt, Yt, class_weight)
sgdClassify(X, Y, Xt, Yt, class_weight)
def training(X, Y, Xt, filename):
# Function actually used to generate predictions for final test sets
# Class weight
class_weight = {0:0.6, 1:0.4}
YPredict1 = linearRegression(X, Y, Xt, None)
YPredict2, YProb2 = logisticRegression(X, Y, Xt, None, class_weight)
YPredict3, YProb3 = sgdClassify(X, Y, Xt, None, class_weight)
util.savePred(YPredict1, YPredict1, "{0}1.csv".format(filename))
util.savePred(YPredict2, YProb2, "{0}2.csv".format(filename))
util.savePred(YPredict3, YProb2, "{0}3.csv".format(filename))
ax.scatter(xcord2, ycord2, s=30, c='green')
x = np.arange(-3.0, 3.0, 0.1)
y = (-theta[0] - theta[1] * x) / theta[2] # z = w0 + w1x1 + w2x2
ax.plot(x,y)
plt.xlabel('X1')
plt.ylabel('X2')
plt.show()
def classifier(theta, x1, x2):
z = theta[0] + theta[1]*x1 + theta[2] * x2
value = regression.sigmoid(z)
return value
dataArr, labelArr = loadDataSet()
theta = regression.logisticRegression(dataArr, labelArr)
theta2 = regression.sga1(np.array(dataArr), labelArr)
print theta
"""
plotBestFit(theta2, dataArr, labelArr)
n = np.shape(dataArr)[0]
for i in range(n):
output = classifier(theta, dataArr[i][1], dataArr[i][2])
if(output >= 0.5):
group = 1
else:
group = 0
print dataArr[i][1], dataArr[i][2], output, "-->", group
y = (-theta[0] - theta[1] * x) / theta[2] # z = w0 + w1x1 + w2x2
ax.plot(x, y)
plt.xlabel('X1')
plt.ylabel('X2')
plt.show()
def classifier(theta, x1, x2):
z = theta[0] + theta[1] * x1 + theta[2] * x2
value = regression.sigmoid(z)
return value
dataArr, labelArr = loadDataSet()
theta = regression.logisticRegression(dataArr, labelArr)
theta2 = regression.sga1(np.array(dataArr), labelArr)
print theta
"""
plotBestFit(theta2, dataArr, labelArr)
n = np.shape(dataArr)[0]
for i in range(n):
output = classifier(theta, dataArr[i][1], dataArr[i][2])
if(output >= 0.5):
group = 1
else:
group = 0
print dataArr[i][1], dataArr[i][2], output, "-->", group
"""
def __init__(self, rng, input, n_in, hidden_layers_sizes, n_out):
'''
self.hiddenLayer = HiddenLayer(
rng=rng,
input=input,
n_in=n_in,
n_out=n,
activation=T.tanh
)
'''
self.params = []
self.sigmoid_layers = []
self.n_layers = len(hidden_layers_sizes)
for i in xrange(self.n_layers):
if i == 0:
input_size = n_in
else:
input_size = hidden_layers_sizes[i - 1]
if i == 0:
layer_input = input
else:
layer_input = self.sigmoid_layers[-1].output
sigmoid_layer = HiddenLayer(rng=rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=T.nnet.sigmoid)
self.sigmoid_layers.append(sigmoid_layer)
self.params.extend(sigmoid_layer.params)
self.logRegressionLayer = logisticRegression(
input=self.sigmoid_layers[-1].output,
n_in=hidden_layers_sizes[-1],
n_out=n_out
)
self.L1 = abs(self.logRegressionLayer.W).sum()
for i in self.sigmoid_layers:
self.L1 += abs(i.W).sum()
'''
self.L1 = (
abs(self.hiddenLayer.W).sum()
+ abs(self.logRegressionLayer.W).sum()
)
'''
self.L2_sqr = abs(self.logRegressionLayer.W ** 2).sum()
for i in self.sigmoid_layers:
self.L2_sqr += abs(i.W ** 2).sum()
'''
self.L2_sqr = (
(self.hiddenLayer.W ** 2).sum()
+ (self.logRegressionLayer.W ** 2).sum()
)
'''
self.negative_log_likelihood = (
self.logRegressionLayer.negative_log_likelihood
)
self.errors = self.logRegressionLayer.mse
self.params.extend(self.logRegressionLayer.params)
