def main():
np.random.seed(100)
X, Y_ = data.sample_gauss_2d(2, 100)
w, b = binlogreg_train(X, Y_)
probabilities = binlogreg_classify(X, w,b)
Y = np.where(probabilities >= .5, 1, 0)
accuracy, recall, precision = data.eval_perf_binary(Y, Y_)
AP = data.eval_AP(Y_[probabilities.argsort()])
print('Acc: {0}\nRecall: {1}\nPrecision: {2}\nAP: {3}\n'.format(accuracy, recall, precision, AP))
return w,b
'''
Arguments
X: data, np.array NxD
w, b: logistic regression parameters
Return values
probs: a posteriori probabilities for c1, dimensions Nx1
'''
def binlogreg_classify(X,w,b):
return data.sigmoid(np.dot(X, w) + b)
np.random.seed(100)
X,Y_ = data.sample_gauss_2d(2, 100)
w,b = binlogreg_train(X, Y_, param_niter=0)
probs = binlogreg_classify(X, w,b)
Y = []
for prob in probs:
if prob < 0.5:
Y.append(False)
else:
Y.append(True)
accuracy, recall, precision = data.eval_perf_binary(Y, Y_)
AP = data.eval_AP(Y_[probs.argsort()])
#print (accuracy, recall, precision, AP)
# train the model
w,b = binlogreg_train(X,Y_)
# evaluate the model n the training dataset
probs = binlogreg_classify(X,w,b)
print(probs)
#predicted labels
Y = np.vstack([ 0 if (probs[i]<0.5) else 1 for i in range(Y_.shape[0])])
#evaluation metrics
accuracy, recall, precision = data.eval_perf_binary(Y, Y_)
print("Accuracy: ",accuracy)
print("Precision: ",precision)
print("Recall: ",recall)
AP = data.eval_AP(np.vstack(Y_[probs.reshape(1,200).argsort()]))
print("Average precision: ",AP)
#graph the decision surface
decfun = binlogreg_decfun(X,w,b)
bbox=(np.min(X, axis=0), np.max(X, axis=0))
data.graph_surface(decfun, bbox, offset=0.5)
# graph the data points
data.graph_data(X, np.hstack(Y_), np.hstack(Y), special=[])
# show the plot
plt.show()
if __name__ == "__main__":
np.random.seed(100)
# get the training dataset
X, Y_ = data.sample_gauss(2, 20)
# train the model
w, b = binlogreg_train(X, Y_)
# evaluate the model on the training dataset
probs = np.array(binlogreg_classify(X, w, b))
Y = np.array([1 if p > 0.5 else 0 for p in probs])
# report performance
accuracy, recall, precision = data.eval_perf_binary(Y, Y_)
AP = data.eval_AP(Y_[np.argsort(probs)])
print(accuracy, recall, precision, AP)
# graph the decision surface
decfun = binlogreg_decfun(w, b)
bbox = (np.min(X, axis=0), np.max(X, axis=0))
data.graph_surface(decfun, bbox, offset=0.5)
# graph the data points
#data.graph_data(X, Y_, Y, special=[])
# show the plot
plt.show()
if __name__ == "__main__":
# inicijaliziraj generatore slučajnih brojeva
np.random.seed(100)
tf.set_random_seed(100)
# instanciraj podatke X i labele Yoh_
X, Y_ = data.sample_gmm_2d(6, 2, 10)
Yoh_ = data.class_to_onehot(Y_)
# izgradi graf:
tflr = TFLogreg(X.shape[1], Yoh_.shape[1], 0.06, 1)
# nauči parametre:
tflr.train(X, Yoh_, 1000)
# dohvati vjerojatnosti na skupu za učenje
probs = tflr.eval(X)
Y = np.argmax(probs, axis=1)
# ispiši performansu (preciznost i odziv po razredima)
accuracy, recall, precision = data.eval_perf_multi(Y, Y_)
AP = data.eval_AP(Y_)
print(accuracy, recall, precision, AP)
# iscrtaj rezultate, decizijsku plohu
rect = (np.min(X, axis=0), np.max(X, axis=0))
data.graph_surface(calc_class, rect, offset=0.5)
data.graph_data(X, Y_, Y, special=[])
plt.show()
def classify(X):
return logreg_classify(X, w, b)
return classify
if __name__=="__main__":
np.random.seed(100)
# get data
# X,Y_ = sample_gmm_2d(4, 2, 30)
X,Y_ = data.sample_gauss_2d(3, 100)
weights, bias = logreg_train(X,Y_)
# get the class predictions
probs = logreg_classify(X, weights, bias)
Y = np.argmax(probs, axis=1)
# Y = myDummyDecision(X)>0.5
accuracy, recall, precision = data.eval_perf_multi(Y, Y_)
AP = data.eval_AP(Y_[Y.argsort()])
print (accuracy, recall, precision, AP)
# graph the decision surface
decfun = logreg_decfun(weights, bias)
rect=(np.min(X, axis=0), np.max(X, axis=0))
data.graph_surface(decfun, rect, offset=0.5)
# graph the data points
data.graph_data(X, Y_, Y, special=[])
plt.show()
