Update LinearClassifier_1.py
This commit is contained in:
xixu-me committed
1 parent
2a7714c33c
commit
361ef59500
1 file changed
+47
-37
@@ -1,44 +1,49 @@
|
||||
import sys
|
||||
sys.path.insert(1, '../')
|
||||
from utils.utils import *
|
||||
|
||||
sys.path.insert(1, "../")
|
||||
from utils.dataset4learners import *
|
||||
from utils.utils import *
|
||||
|
||||
|
||||
class LinearClassifier:
|
||||
def learn(self, learning_rate, epochs):
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
def predict(self, x):
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
|
||||
|
||||
class PerceptionLinearLearner(LinearClassifier):
|
||||
"""
|
||||
Perception linear classifier: hard threshold
|
||||
"""
|
||||
|
||||
def __init__(self, dataset, learning_rate=0.01, epochs=100):
|
||||
self.idx_i = dataset.inputs
|
||||
self.idx_t = dataset.target
|
||||
self.examples = dataset.examples
|
||||
self.num_examples = len(self.examples)
|
||||
# initialize random weights
|
||||
self.w = random_weights(min_value=-0.5, max_value=0.5, num_weights=len(self.idx_i) + 1)
|
||||
self.w = random_weights(
|
||||
min_value=-0.5, max_value=0.5, num_weights=len(self.idx_i) + 1
|
||||
)
|
||||
# learning loop
|
||||
self.learn(learning_rate, epochs)
|
||||
|
||||
|
||||
def learn(self, learning_rate, epochs):
|
||||
""" learning loop """
|
||||
"""learning loop"""
|
||||
|
||||
def loss(example, w, idx_i, idx_t):
|
||||
""" error: difference between estimation and true value """
|
||||
"""error: difference between estimation and true value"""
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
def update(w, learning_rate, err, X_col, num_examples):
|
||||
""" update weights """
|
||||
"""update weights"""
|
||||
for i in range(len(w)):
|
||||
w[i] = w[i] - learning_rate * (np.dot(err, X_col[i]) / num_examples)
|
||||
|
||||
|
||||
def homogeneous(num_examples):
|
||||
""" build homogeneous coordinates """
|
||||
"""build homogeneous coordinates"""
|
||||
raise NotImplementedError
|
||||
|
||||
X_col = homogeneous(self.num_examples)
|
||||
@@ -52,7 +57,7 @@ class PerceptionLinearLearner(LinearClassifier):
|
||||
update(self.w, learning_rate, err, X_col, self.num_examples)
|
||||
|
||||
def predict(self, x):
|
||||
""" make prediction """
|
||||
"""make prediction"""
|
||||
return int(np.dot(self.w, [1] + x))
|
||||
|
||||
|
||||
@@ -63,24 +68,27 @@ class LogisticLinearLeaner(LinearClassifier):
|
||||
self.examples = dataset.examples
|
||||
self.num_examples = len(self.examples)
|
||||
# initialize random weights
|
||||
self.w = random_weights(min_value=-0.5, max_value=0.5, num_weights=len(self.idx_i) + 1)
|
||||
self.w = random_weights(
|
||||
min_value=-0.5, max_value=0.5, num_weights=len(self.idx_i) + 1
|
||||
)
|
||||
# learning loop
|
||||
self.learn(learning_rate, epochs)
|
||||
|
||||
|
||||
def learn(self, learning_rate, epochs):
|
||||
""" learning loop """
|
||||
"""learning loop"""
|
||||
|
||||
def loss(example, w, idx_i, idx_t, h):
|
||||
""" error: difference between estimation and true value """
|
||||
"""error: difference between estimation and true value"""
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
def update(w, learning_rate, err, h, X_col, num_examples):
|
||||
""" update weights """
|
||||
"""update weights"""
|
||||
for i in range(len(w)):
|
||||
buffer = [x * y for x, y in zip(err, h)]
|
||||
w[i] = w[i] - learning_rate * (np.dot(buffer, X_col[i]) / num_examples)
|
||||
|
||||
def homogeneous(num_examples):
|
||||
""" build homogeneous coordinates """
|
||||
"""build homogeneous coordinates"""
|
||||
raise NotImplementedError
|
||||
|
||||
X_col = homogeneous(self.num_examples)
|
||||
@@ -95,35 +103,37 @@ class LogisticLinearLeaner(LinearClassifier):
|
||||
update(self.w, learning_rate, err, h, X_col, self.num_examples)
|
||||
|
||||
def predict(self, x):
|
||||
""" make prediction """
|
||||
"""make prediction"""
|
||||
return int(np.dot(self.w, [1] + x))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
iris = DataSet(name="iris")
|
||||
iris.classes_to_numbers()
|
||||
|
||||
tests = [([5, 3, 1, 0.1], 0),
|
||||
([5, 3.5, 1, 0], 0),
|
||||
([6, 3, 4, 1.1], 1),
|
||||
([6, 2, 3.5, 1], 1),
|
||||
([7.5, 4, 6, 2], 2),
|
||||
([7, 3, 6, 2.5], 2)]
|
||||
|
||||
print(f'===================\nperceptron:')
|
||||
|
||||
tests = [
|
||||
([5, 3, 1, 0.1], 0),
|
||||
([5, 3.5, 1, 0], 0),
|
||||
([6, 3, 4, 1.1], 1),
|
||||
([6, 2, 3.5, 1], 1),
|
||||
([7.5, 4, 6, 2], 2),
|
||||
([7, 3, 6, 2.5], 2),
|
||||
]
|
||||
|
||||
print(f"===================\nperceptron:")
|
||||
perceptron = PerceptionLinearLearner(iris)
|
||||
g = grade_learner(perceptron, tests)
|
||||
print(f' learner grade: {g}')
|
||||
print(f" learner grade: {g}")
|
||||
# assert g > 1. / 2
|
||||
e = err_ratio(perceptron, iris)
|
||||
print(f' error ration: {e}')
|
||||
print(f" error ration: {e}")
|
||||
# assert e < 0.4
|
||||
|
||||
print(f'===================\nlogistic:')
|
||||
|
||||
print(f"===================\nlogistic:")
|
||||
logisticer = LogisticLinearLeaner(iris)
|
||||
g = grade_learner(logisticer, tests)
|
||||
print(f' learner grade: {g}')
|
||||
print(f" learner grade: {g}")
|
||||
# assert g > 1. / 2
|
||||
e = err_ratio(logisticer, iris)
|
||||
print(f' error ration: {e}')
|
||||
print(f" error ration: {e}")
|
||||
# assert e < 0.4
|
||||
Reference in new issue
Block a user