init

car/car.data

@@ -0,0 +1,1728 @@
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+3,1,1,8,10,3,3

car/mlp_tf.py

@@ -0,0 +1,98 @@
+import tensorflow as tf
+import numpy as np
+
+
+def get_one_hot(targets, nb_classes):
+    res = np.eye(nb_classes)[np.array(targets).reshape(-1)]
+    return res.reshape(list(targets.shape)+[nb_classes])
+
+data_slice = 5
+
+def load():
+    raw_data = np.loadtxt('car.data', delimiter=',')
+    data_size = len(raw_data)
+    np.random.shuffle(raw_data)
+    data = np.array_split(raw_data, data_slice)
+    ret = [np.split(i, [1,], axis=1) for i in data]
+    return ret
+
+# learning params
+learning_rate = 0.1
+training_epochs = 400
+batch_size = 4
+
+# network params
+n_hidden_1 = 32
+n_hidden_2 = 16
+n_input = 6
+n_classses = 4
+
+data_pieces = load()
+val_idx = 0
+
+train_label = np.concatenate(
+  [data_pieces[i][0] for i in list(range(data_slice))[:val_idx] + list(range(data_slice))[val_idx+1:]]
+)
+# train_label = train_label - 1
+train_label = train_label.flatten().astype(int)
+train_label = get_one_hot(train_label, n_classses)
+
+train_data = np.concatenate(
+  [data_pieces[i][1] for i in list(range(data_slice))[:val_idx] + list(range(data_slice))[val_idx+1:]]
+)
+
+val_label = data_pieces[val_idx][0]
+val_data = data_pieces[val_idx][1]
+# val_label = val_label - 1
+val_label = val_label.flatten().astype(int)
+val_label = get_one_hot(val_label, n_classses)
+
+
+
+# model
+x = tf.placeholder("float", [None, n_input])
+y = tf.placeholder("float", [None, n_classses])
+
+def mlp(_X, _weights, _biases):
+    layer1 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1']))
+    layer2 = tf.nn.sigmoid(tf.add(tf.matmul(layer1, _weights['h2']), _biases['b2']))
+    # layer2 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights['h2']), _biases['b2']))
+    return tf.matmul(layer2, _weights['out']) + _biases['out']
+
+weights = {
+    'h1' : tf.Variable(tf.random_normal([n_input, n_hidden_1])),
+    'h2' : tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
+    'out' : tf.Variable(tf.random_normal([n_hidden_2, n_classses]))
+}
+biases = {
+    'b1' : tf.Variable(tf.random_normal([n_hidden_1])),
+    'b2' : tf.Variable(tf.random_normal([n_hidden_2])),
+    'out' : tf.Variable(tf.random_normal([n_classses]))
+}
+
+pred = mlp(x, weights, biases)
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=pred))
+optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
+
+init = tf.global_variables_initializer()
+
+train_data_batches = np.array_split(train_data, len(train_data) // batch_size)
+train_label_batches = np.array_split(train_label, len(train_label) // batch_size)
+
+correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
+
+with tf.Session() as sess:
+    sess.run(init)
+    #Training cycle
+    for epoch in range(training_epochs):
+        for i, j in zip(train_data_batches, train_label_batches):
+            sess.run(optimizer, feed_dict={x: i, y: j})
+        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
+        if (accuracy.eval({x: train_data, y: train_label}) > 0.95):
+            print(epoch)
+        if (accuracy.eval({x: train_data, y: train_label}) > 0.99):
+            print(epoch)
+            print("Val Accuracy:", accuracy.eval({x: val_data, y: val_label}))
+            exit()
+        #print("Train Accuracy:", accuracy.eval({x: train_data, y: train_label}))
+        #

car/pla_np.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python3
+import numpy as np
+
+c = 0.1
+LIMIT = 100
+print_epoches = [5, 10, 100, 1000]
+DATA_SLICE = 5
+NUM_CLASS = 4
+INPUT_FEATURE = 6
+
+def load():
+    raw_data = np.loadtxt('car.data', delimiter=',')
+    data_size = len(raw_data)
+    np.random.shuffle(raw_data)
+    data = np.array_split(raw_data, DATA_SLICE)
+    ret = [np.split(i, [1,], axis=1) for i in data]
+    return ret
+
+data_pieces = load()
+
+val_idx = 0
+
+train_label = np.concatenate(
+  [data_pieces[i][0] for i in list(range(DATA_SLICE))[:val_idx] + list(range(DATA_SLICE))[val_idx+1:]]
+)
+train_label = train_label - 1
+train_label = train_label.flatten().astype(int)
+
+train_data = np.concatenate(
+  [data_pieces[i][1] for i in list(range(DATA_SLICE))[:val_idx] + list(range(DATA_SLICE))[val_idx+1:]]
+)
+
+
+val_label = data_pieces[val_idx][0]
+val_data = data_pieces[val_idx][1]
+val_label = val_label - 1
+val_label = val_label.flatten().astype(int)
+
+v = np.array(val_data, dtype='float32')
+v = np.concatenate((v, np.ones((len(v), 1))), axis=1)
+
+def pla(train_data, train_label, val_data, val_label, picked_class):
+    x = np.array(train_data, dtype='float32')
+    x = np.concatenate((x, np.ones((len(x), 1))), axis=1)
+
+    for i in range(len(x)):
+        if train_label[i] != picked_class:
+            x[i] *= -1
+    w = np.random.rand(INPUT_FEATURE + 1)
+    for j in range(LIMIT):
+        flag = False
+        for i in x:
+            z = sum(i * w)
+            if z <= 0:
+                w = w + c * i
+                flag = True
+        if not flag:
+            print('')
+            break
+        #if j in print_epoches:
+        print(j)
+        print('train acc:', np.sum(np.sum(x * w, axis=1) > 0) / len(x))
+        print('val acc:', np.sum((np.sum(v * w, axis=1) > 0) == (val_label == picked_class)) / len(v))
+    return w
+
+
+w = [pla(train_data, train_label, val_data, val_label, i) for i in range(NUM_CLASS)]
+pred = [np.sum(v * w[i], axis=1) > 0 for i in range(NUM_CLASS)]
+print('val acc:', sum([all([pred[j][i] == (j == val_label[i]) for j in range(NUM_CLASS)]) for i in range(len(val_label))]) / len(val_label))

car/svm.py

@@ -0,0 +1,37 @@
+import numpy as np
+from sklearn import svm
+
+def get_one_hot(targets, nb_classes):
+    res = np.eye(nb_classes)[np.array(targets).reshape(-1)]
+    return res.reshape(list(targets.shape)+[nb_classes])
+
+data_slice = 5
+val_idx = 0
+def load():
+    raw_data = np.loadtxt('car.data', delimiter=',')
+    data_size = len(raw_data)
+    np.random.shuffle(raw_data)
+    data = np.array_split(raw_data, data_slice)
+    ret = [np.split(i, [1,], axis=1) for i in data]
+    return ret
+
+data_pieces = load()
+train_label = np.concatenate(
+  [data_pieces[i][0] for i in list(range(data_slice))[:val_idx] + list(range(data_slice))[val_idx+1:]]
+)
+train_label = train_label - 1
+train_label = train_label.flatten().astype(int)
+
+train_data = np.concatenate(
+  [data_pieces[i][1] for i in list(range(data_slice))[:val_idx] + list(range(data_slice))[val_idx+1:]]
+)
+
+val_label = data_pieces[val_idx][0]
+val_data = data_pieces[val_idx][1]
+val_label = val_label - 1
+val_label = val_label.flatten().astype(int)
+
+
+clf = svm.SVC(gamma='scale')
+clf.fit(train_data, train_label)
+print('val_acc', sum(clf.predict(val_data) == val_label) / len(val_label))

spect/SPECT.test

@@ -0,0 +1,187 @@
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spect/mlp_tf.py

@@ -0,0 +1,92 @@
+import tensorflow as tf
+import numpy as np
+
+
+def get_one_hot(targets, nb_classes):
+    res = np.eye(nb_classes)[np.array(targets).reshape(-1)]
+    return res.reshape(list(targets.shape)+[nb_classes])
+
+
+def load(file):
+    raw_data = np.loadtxt(file, delimiter=',')
+    data_size = len(raw_data)
+    np.random.shuffle(raw_data)
+    ret = np.split(raw_data, [1,], axis=1)
+    return ret
+
+train_pieces = load('SPECT.train')
+train_label = train_pieces[0]
+train_data = train_pieces[1]
+# train_label = train_label - 1
+train_label = train_label.flatten().astype(int)
+train_label = get_one_hot(train_label, 2)
+
+val_pieces = load('SPECT.test')
+
+val_label = val_pieces[0]
+val_data = val_pieces[1]
+# val_label = val_label - 1
+val_label = val_label.flatten().astype(int)
+val_label2 = val_label.flatten().astype(int)
+val_label = get_one_hot(val_label, 2)
+
+# learning params
+learning_rate = 0.005
+training_epochs = 200
+batch_size = 16
+
+# network params
+n_hidden_1 = 32
+n_hidden_2 = 8
+n_input = 22
+n_classses = 2
+
+# model
+x = tf.placeholder("float", [None, n_input])
+y = tf.placeholder("float", [None, n_classses])
+
+def mlp(_X, _weights, _biases):
+    layer1 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1']))
+    layer2 = tf.nn.sigmoid(tf.add(tf.matmul(layer1, _weights['h2']), _biases['b2']))
+    return tf.matmul(layer2, _weights['out']) + _biases['out']
+
+weights = {
+    'h1' : tf.Variable(tf.random_normal([n_input, n_hidden_1])),
+    'h2' : tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
+    'out' : tf.Variable(tf.random_normal([n_hidden_2, n_classses]))
+}
+biases = {
+    'b1' : tf.Variable(tf.random_normal([n_hidden_1])),
+    'b2' : tf.Variable(tf.random_normal([n_hidden_2])),
+    'out' : tf.Variable(tf.random_normal([n_classses]))
+}
+
+pred = mlp(x, weights, biases)
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=pred))
+optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
+
+init = tf.global_variables_initializer()
+
+train_data_batches = np.array_split(train_data, len(train_data) // batch_size)
+train_label_batches = np.array_split(train_label, len(train_label) // batch_size)
+
+correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
+pred_label = tf.argmax(pred, 1)
+with tf.Session() as sess:
+    sess.run(init)
+    #Training cycle
+    for epoch in range(training_epochs):
+        for i, j in zip(train_data_batches, train_label_batches):
+            sess.run(optimizer, feed_dict={x: i, y: j})
+        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
+        pl = pred_label.eval({x: val_data, y: val_label})
+
+        TP = sum(pl[i] == 1 and val_label2[i] == 1 for i in range(len(val_label2)))
+        FP = sum(pl[i] == 1 and val_label2[i] == 0 for i in range(len(val_label2)))
+        FN = sum(pl[i] == 0 and val_label2[i] == 1 for i in range(len(val_label2)))
+
+        P = TP / (TP + FP)
+        R = TP / (TP + FN)
+        print("Train Accuracy:", accuracy.eval({x: train_data, y: train_label}))
+        print("Val Accuracy:", accuracy.eval({x: val_data, y: val_label}))
+        print('F1', 2 * P * R / (P + R))

spect/pla_np.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+import numpy as np
+
+c = 0.1
+LIMIT = 100
+print_epoches = [5, 10, 100, 1000]
+DATA_SLICE = 5
+NUM_CLASS = 1
+INPUT_FEATURE = 22
+
+def get_one_hot(targets, nb_classes):
+    res = np.eye(nb_classes)[np.array(targets).reshape(-1)]
+    return res.reshape(list(targets.shape)+[nb_classes])
+
+def load(file):
+    raw_data = np.loadtxt(file, delimiter=',')
+    data_size = len(raw_data)
+    np.random.shuffle(raw_data)
+    ret = np.split(raw_data, [1,], axis=1)
+    return ret
+
+train_pieces = load('SPECT.train')
+train_label = train_pieces[0]
+train_data = train_pieces[1]
+train_label = train_label.flatten().astype(int)
+
+
+val_pieces = load('SPECT.test')
+
+val_label = val_pieces[0]
+val_data = val_pieces[1]
+val_label = val_label.flatten().astype(int)
+
+v = np.array(val_data, dtype='float32')
+v = np.concatenate((v, np.ones((len(v), 1))), axis=1)
+
+def pla(train_data, train_label, val_data, val_label, picked_class):
+    x = np.array(train_data, dtype='float32')
+    x = np.concatenate((x, np.ones((len(x), 1))), axis=1)
+
+    for i in range(len(x)):
+        if train_label[i] != picked_class:
+            x[i] *= -1
+    w = np.random.rand(INPUT_FEATURE + 1)
+    for j in range(LIMIT):
+        flag = False
+        for i in x:
+            z = sum(i * w)
+            if z <= 0:
+                w = w + c * i
+                flag = True
+        if not flag:
+            print('')
+            break
+        #if j in print_epoches:
+        print(j)
+        print('train acc:', np.sum(np.sum(x * w, axis=1) > 0) / len(x))
+        print('val acc:', np.sum((np.sum(v * w, axis=1) > 0) == (val_label == picked_class)) / len(v))
+    return w
+
+
+w = [pla(train_data, train_label, val_data, val_label, i) for i in range(NUM_CLASS)]
+pred = [np.sum(v * w[i], axis=1) > 0 for i in range(NUM_CLASS)]
+TP = sum(pred[0][i] == 1 and val_label[i] == 1 for i in range(len(val_label)))
+FP = sum(pred[0][i] == 1 and val_label[i] == 0 for i in range(len(val_label)))
+FN = sum(pred[0][i] == 0 and val_label[i] == 1 for i in range(len(val_label)))
+P = TP / (TP + FP)
+R = TP / (TP + FN)
+print('val acc:', sum([all([pred[j][i] == (j == val_label[i]) for j in range(NUM_CLASS)]) for i in range(len(val_label))]) / len(val_label))
+print('F1', 2 * P * R / (P + R))

spect/svm.py

@@ -0,0 +1,36 @@
+import numpy as np
+from sklearn import svm
+
+def get_one_hot(targets, nb_classes):
+    res = np.eye(nb_classes)[np.array(targets).reshape(-1)]
+    return res.reshape(list(targets.shape)+[nb_classes])
+
+
+def load(file):
+    raw_data = np.loadtxt(file, delimiter=',')
+    data_size = len(raw_data)
+    np.random.shuffle(raw_data)
+    ret = np.split(raw_data, [1,], axis=1)
+    return ret
+
+train_pieces = load('SPECT.train')
+train_label = train_pieces[0]
+train_data = train_pieces[1]
+train_label = train_label.flatten().astype(int)
+
+val_pieces = load('SPECT.test')
+
+val_label = val_pieces[0]
+val_data = val_pieces[1]
+val_label = val_label.flatten().astype(int)
+
+clf = svm.SVC(gamma='scale')
+clf.fit(train_data, train_label)
+pred = clf.predict(val_data)
+TP = sum(pred[i] == 1 and val_label[i] == 1 for i in range(len(val_label)))
+FP = sum(pred[i] == 1 and val_label[i] == 0 for i in range(len(val_label)))
+FN = sum(pred[i] == 0 and val_label[i] == 1 for i in range(len(val_label)))
+P = TP / (TP + FP)
+R = TP / (TP + FN)
+print('val_acc', sum(pred == val_label) / len(val_label))
+print('F1', 2 * P * R / (P + R))

wine/mlp_tf.py

@@ -0,0 +1,91 @@
+import tensorflow as tf
+import numpy as np
+
+
+def get_one_hot(targets, nb_classes):
+    res = np.eye(nb_classes)[np.array(targets).reshape(-1)]
+    return res.reshape(list(targets.shape)+[nb_classes])
+
+
+
+data_slice = 5
+
+def load():
+    raw_data = np.loadtxt('wine.data', delimiter=',')
+    data_size = len(raw_data)
+    np.random.shuffle(raw_data)
+    data = np.array_split(raw_data, data_slice)
+    ret = [np.split(i, [1,], axis=1) for i in data]
+    return ret
+
+data_pieces = load()
+val_idx = 0
+
+train_label = np.concatenate(
+  [data_pieces[i][0] for i in list(range(data_slice))[:val_idx] + list(range(data_slice))[val_idx+1:]]
+)
+train_label = train_label - 1
+train_label = train_label.flatten().astype(int)
+train_label = get_one_hot(train_label, 3)
+
+train_data = np.concatenate(
+  [data_pieces[i][1] for i in list(range(data_slice))[:val_idx] + list(range(data_slice))[val_idx+1:]]
+)
+
+val_label = data_pieces[val_idx][0]
+val_data = data_pieces[val_idx][1]
+val_label = val_label - 1
+val_label = val_label.flatten().astype(int)
+val_label = get_one_hot(val_label, 3)
+
+# learning params
+learning_rate = 0.001
+training_epochs = 200
+batch_size = 4
+
+# network params
+n_hidden_1 = 256
+n_hidden_2 = 128
+n_input = 13
+n_classses = 3
+
+# model
+x = tf.placeholder("float", [None, n_input])
+y = tf.placeholder("float", [None, n_classses])
+
+def mlp(_X, _weights, _biases):
+    layer1 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1']))
+    layer2 = tf.nn.sigmoid(tf.add(tf.matmul(layer1, _weights['h2']), _biases['b2']))
+    return tf.matmul(layer2, _weights['out']) + _biases['out']
+
+weights = {
+    'h1' : tf.Variable(tf.random_normal([n_input, n_hidden_1])),
+    'h2' : tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
+    'out' : tf.Variable(tf.random_normal([n_hidden_2, n_classses]))
+}
+biases = {
+    'b1' : tf.Variable(tf.random_normal([n_hidden_1])),
+    'b2' : tf.Variable(tf.random_normal([n_hidden_2])),
+    'out' : tf.Variable(tf.random_normal([n_classses]))
+}
+
+pred = mlp(x, weights, biases)
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=pred))
+optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
+
+init = tf.global_variables_initializer()
+
+train_data_batches = np.array_split(train_data, len(train_data) // batch_size)
+train_label_batches = np.array_split(train_label, len(train_label) // batch_size)
+
+correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
+
+with tf.Session() as sess:
+    sess.run(init)
+    #Training cycle
+    for epoch in range(training_epochs):
+        for i, j in zip(train_data_batches, train_label_batches):
+            sess.run(optimizer, feed_dict={x: i, y: j})
+        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
+        print("Train Accuracy:", accuracy.eval({x: train_data, y: train_label}))
+        print("Val Accuracy:", accuracy.eval({x: val_data, y: val_label}))

wine/pla_np.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python3
+import numpy as np
+
+c = 0.1
+LIMIT = 20
+print_epoches = [5, 10, 100, 1000]
+DATA_SLICE = 5
+NUM_CLASS = 3
+INPUT_FEATURE = 13
+
+def load():
+    raw_data = np.loadtxt('wine.data', delimiter=',')
+    data_size = len(raw_data)
+    np.random.shuffle(raw_data)
+    data = np.array_split(raw_data, DATA_SLICE)
+    ret = [np.split(i, [1,], axis=1) for i in data]
+    return ret
+
+data_pieces = load()
+
+val_idx = 0
+
+train_label = np.concatenate(
+  [data_pieces[i][0] for i in list(range(DATA_SLICE))[:val_idx] + list(range(DATA_SLICE))[val_idx+1:]]
+)
+train_label = train_label - 1
+train_label = train_label.flatten().astype(int)
+
+train_data = np.concatenate(
+  [data_pieces[i][1] for i in list(range(DATA_SLICE))[:val_idx] + list(range(DATA_SLICE))[val_idx+1:]]
+)
+
+
+val_label = data_pieces[val_idx][0]
+val_data = data_pieces[val_idx][1]
+val_label = val_label - 1
+val_label = val_label.flatten().astype(int)
+
+v = np.array(val_data, dtype='float32')
+v = np.concatenate((v, np.ones((len(v), 1))), axis=1)
+
+def pla(train_data, train_label, val_data, val_label, picked_class):
+    x = np.array(train_data, dtype='float32')
+    x = np.concatenate((x, np.ones((len(x), 1))), axis=1)
+
+    for i in range(len(x)):
+        if train_label[i] != picked_class:
+            x[i] *= -1
+    w = np.random.rand(INPUT_FEATURE + 1)
+    for j in range(LIMIT):
+        flag = False
+        for i in x:
+            z = sum(i * w)
+            if z <= 0:
+                w = w + c * i
+                flag = True
+        if not flag:
+            print('')
+            break
+        #if j in print_epoches:
+        print(j)
+        print('train acc:', np.sum(np.sum(x * w, axis=1) > 0) / len(x))
+        print('val acc:', np.sum((np.sum(v * w, axis=1) > 0) == (val_label == picked_class)) / len(v))
+    return w
+
+
+w = [pla(train_data, train_label, val_data, val_label, i) for i in range(NUM_CLASS)]
+pred = [np.sum(v * w[i], axis=1) > 0 for i in range(NUM_CLASS)]
+print('val acc:', sum([all([pred[j][i] == (j == val_label[i]) for j in range(NUM_CLASS)]) for i in range(len(val_label))]) / len(val_label))

wine/svm.py

@@ -0,0 +1,40 @@
+import numpy as np
+from sklearn import svm
+
+def get_one_hot(targets, nb_classes):
+    res = np.eye(nb_classes)[np.array(targets).reshape(-1)]
+    return res.reshape(list(targets.shape)+[nb_classes])
+
+
+
+data_slice = 5
+
+def load():
+    raw_data = np.loadtxt('wine.data', delimiter=',')
+    data_size = len(raw_data)
+    np.random.shuffle(raw_data)
+    data = np.array_split(raw_data, data_slice)
+    ret = [np.split(i, [1,], axis=1) for i in data]
+    return ret
+
+data_pieces = load()
+val_idx = 0
+
+train_label = np.concatenate(
+  [data_pieces[i][0] for i in list(range(data_slice))[:val_idx] + list(range(data_slice))[val_idx+1:]]
+)
+train_label = train_label - 1
+train_label = train_label.flatten().astype(int)
+
+train_data = np.concatenate(
+  [data_pieces[i][1] for i in list(range(data_slice))[:val_idx] + list(range(data_slice))[val_idx+1:]]
+)
+
+val_label = data_pieces[val_idx][0]
+val_data = data_pieces[val_idx][1]
+val_label = val_label - 1
+val_label = val_label.flatten().astype(int)
+
+clf = svm.SVC(gamma='scale')
+clf.fit(train_data, train_label)
+print('val_acc', sum(clf.predict(val_data) == val_label) / len(val_label))

wine/wine.data

@@ -0,0 +1,178 @@
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