Duk's Computer science

List<Integer> linkedList = new LinkedList<>();

List<Integer> arrayList = new ArrayList<>();

for (int i = 0; i < 100000; i++) {

linkedList.add(i);

arrayList.add(i);

}

for (int i = 0; i < list.size(); i++) {

Integer value = list.get(i);

}

for (Integer value : list) {

}

Iterator<Integer> iterator = list.iterator();

while (iterator.hasNext()) {

Integer value = iterator.next();

}

list.forEach((value) -> {

});

public class QuickSort implements ISort {

    @Override

    public void sort(Comparable[] array) {

        quickSort(array, 0, array.length - 1);

    }

    private void quickSort(Comparable[] array, int startIndex, int endIndex) {

        if (startIndex >= endIndex) {

            return;

        }

        final Comparable pivot = array[startIndex];

        int left = startIndex + 1;

        int right = endIndex;

        do {

            while (array[left].compareTo(pivot) < 0 && left < endIndex) {

                left++;

            }

            while (array[right].compareTo(pivot) > 0 && startIndex < right) {

                right--;

            }

            if (left < right) {

                swap(array, left, right);

            }

        } while (left < right && left < endIndex && startIndex < right);

        swap(array, startIndex, right);

        quickSort(array, startIndex, right - 1);

        quickSort(array, right + 1, endIndex);

    }

    private void swap(Comparable[] array, int i, int j) {

        Comparable temp = array[i];

        array[i] = array[j];

        array[j] = temp;

    }

}

public class Singleton {

    private Singleton() {

    }

    private static Singleton sInstance;

    public static Singleton getInstance() {

        if (sInstance == null) {

            sInstance = new Singleton();

        }

        return sInstance;

    }

}

public class Singleton {

    private Singleton() {

    }

    private static Singleton sInstanceSync;

    public static synchronized Singleton getInstanceSync() {

        if (sInstanceSync == null) {

            sInstanceSync = new Singleton();

        }

        return sInstanceSync;

    }

}

public class Singleton {

    private Singleton() {

    }

    private static final Singleton STATIC_INSTANCE = new Singleton();

    public static Singleton getInstanceStatic() {

        return STATIC_INSTANCE;

    }

}

public class Singleton {

    private Singleton() {

    }

    private static class LazyHolder {

        public static final Singleton INSTANCE = new Singleton();

    }

    public static Singleton getInstanceLazyHolder() {

        return LazyHolder.INSTANCE;

    }

}

public enum SingletonEnum {

    INSTANCE;

    // TODO: your methods

}

# TensorFlow 불러오기

import tensorflow as tf

# Matplotlib 불러오기

import matplotlib

# Matplotlib backend 설정

matplotlib.use('TkAgg')

import matplotlib.pyplot as plt

X = [1, 2, 3]

Y = [1, 2, 3]

W = tf.placeholder(tf.float32)

# H(x) = Wx

hypothesis = X * W

# Cost: 1/m * ∑(H(x) - y)^2

cost = tf.reduce_mean(tf.square(hypothesis - Y))

# Session 생성

sess = tf.Session()

# Global variable 초기화

sess.run(tf.global_variables_initializer())

W_val = []

cost_val = []

# -3 ~ 5 까지 W(기울기)를 0.1씩 바꿔보면서 W에 따른 cost 값을 구한다

for i in range(-30, 50):

    feed_W = i * 0.1

    curr_cost, curr_W = sess.run([cost, W], feed_dict={W: feed_W})

    W_val.append(curr_W)

    cost_val.append(curr_cost)

# matplotlib으로 표현하기

plt.plot(W_val, cost_val)

plt.show()

# gradient를 계산한 값 구해서 적용하기(compute_gradient, apply_gradient)

# Loading TensorFlow
import tensorflow as tf

x_data = [1, 2, 3]
y_data = [1, 2, 3]

# W = tf.Variable(5.0)
W = tf.Variable(-3.0)
X = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)

# H(x) = Wx
hypothesis = X * W

# Cost: 1/m * ∑(H(x) - y)^2
cost = tf.reduce_mean(tf.square(hypothesis - Y))
gradient = tf.reduce_mean(((W * X - Y) * X) * 2)

# Gradient Descent Magic
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)

# Gradient 계산값 구하여 수동 적용
# train = optimizer.minimize(cost)
gvs = optimizer.compute_gradients(cost)
# TODO: gvs 수정
train = optimizer.apply_gradients(gvs)

# Session 생성
sess = tf.Session()

# Global variable 초기화
sess.run(tf.global_variables_initializer())

for step in range(10):
    print(step, sess.run([gradient, W, gvs], feed_dict={X: x_data, Y: y_data}))
    sess.run(train, feed_dict={X: x_data, Y: y_data})

'''
Result
0 [-37.333332, -3.0, [(-37.333336, -3.0)]]
1 [-2.4888866, 0.73333359, [(-2.4888866, 0.73333359)]]
2 [-0.1659257, 0.98222226, [(-0.16592571, 0.98222226)]]
3 [-0.011061668, 0.99881482, [(-0.011061668, 0.99881482)]]
4 [-0.00073742867, 0.99992096, [(-0.00073742867, 0.99992096)]]
5 [-4.9630802e-05, 0.9999947, [(-4.9630802e-05, 0.9999947)]]
6 [-3.0994415e-06, 0.99999964, [(-3.0994415e-06, 0.99999964)]]
7 [-6.7551929e-07, 0.99999994, [(-6.7551935e-07, 0.99999994)]]
8 [0.0, 1.0, [(0.0, 1.0)]]
9 [0.0, 1.0, [(0.0, 1.0)]]
'''

$ source [설치한 경로]/bin/activate

(설치폴더이름) $ pip3 install matplotlib

Traceback (most recent call last):

  File "/Users/[user]/workspace/learning_TensorFlow/cpu_python3/learning_lab3_1.py", line 3, in <module>

    import matplotlib.pyplot as plt

  File "/Users/[user]/tf_py_3/lib/python3.6/site-packages/matplotlib/pyplot.py", line 115, in <module>

    _backend_mod, new_figure_manager, draw_if_interactive, _show = pylab_setup()

  File "/Users/[user]/tf_py_3/lib/python3.6/site-packages/matplotlib/backends/__init__.py", line 32, in pylab_setup

    globals(),locals(),[backend_name],0)

  File "/Users/[user]/tf_py_3/lib/python3.6/site-packages/matplotlib/backends/backend_macosx.py", line 19, in <module>

    from matplotlib.backends import _macosx

RuntimeError: Python is not installed as a framework. The Mac OS X backend will not be able to function correctly if Python is not installed as a framework. See the Python documentation for more information on installing Python as a framework on Mac OS X. Please either reinstall Python as a framework, or try one of the other backends. If you are using (Ana)Conda please install python.app and replace the use of 'python' with 'pythonw'. See 'Working with Matplotlib on OSX' in the Matplotlib FAQ for more information.

1) 스크립트 상에서

import matplotlib

matplotlib.use('TkAgg')

import matplotlib.pyplot as plt

2) virtualenv bash 상에서

(설치폴더이름) $ echo "backend: TkAgg" >> ~/.matplotlib/matplotlibrc

# virtualenv에서 Python3 실행

$ source ~tf_py_3/bin/activate

(tf_py_3) $ python

Python 3.6.0 (v3.6.0:41df79263a11, Dec 22 2016, 17:23:13) 

[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin

Type "help", "copyright", "credits" or "license" for more information.

>>> 

# TensorFlow 실행

>>> import tensorflow as tf

# Version 확인

>>> tf.__version__

'1.1.0'

>>>

# 상수의  Default node 선언

>>> hellotensorflow = tf.constant("Hello TensorFlow!")

# Session을 생성

>>> sess = tf.Session()

# Python Print 명령으로 출력

>>> print(sess.run(hellotensorflow))

b'Hello, TensorFlow'

# Session 닫기

>>> sess.close()

node1 = tf.constant(3.0, tf.float32)

node2 = tf.constant(4.0) # 암시적으로 tf.float32으로 설정된다

node3 = tf.add(node1, node2)

# Tensor 객체의 정보를 찍는다.

print("node1=", node1, "node2=", node2)

print("node3=", node3)

# node1= Tensor("Const_1:0", shape=(), dtype=float32) node2= Tensor("Const_2:0", shape=(), dtype=float32)

# node3= Tensor("Add:0", shape=(), dtype=float32)

sess = tf.Session()

print("sess.run([node1, node2])=", sess.run([node1, node2]))

print("sess.run(node3)=", sess.run(node3))

sess.close()

# sess.run([node1, node2])= [3.0, 4.0]

# sess.run(node3)= 7.0

a = tf.placeholder(tf.float32)

b = tf.placeholder(tf.float32)

adder_node = a + b # tf.add(a, b) 와 같다

sess = tf.Session()

print(sess.run(adder_node, feed_dict={a: 3, b: 4.5}))

print(sess.run(adder_node, feed_dict={a: [1, 3], b: [2, 4]}))

sess.close()

# 7.5

# [ 3. 7.]

# TensorFlow 실행

import tensorflow as tf

# train 값 사전 정의

x_train = [1, 2, 3]

y_train = [1, 2, 3]

# 가설: H(x) = Wx + b

W = tf.Variable(tf.random_normal([1]), name='weight')

b = tf.Variable(tf.random_normal([1]), name='bias')

hypothesis = x_train * W + b

# Cost: 1/m * H(x)

cost = tf.reduce_mean(tf.square(hypothesis - y_train))

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)

train = optimizer.minimize(cost)

sess = tf.Session()

sess.run(tf.global_variables_initializer())

for step in range(2001):

    sess.run(train)

    if step % 20 == 0:

        print(step, sess.run(cost), sess.run(W), sess.run(b))

# TensorFlow 실행

import tensorflow as tf

# train 할 값 나중에 정의

# shape=[None] : 여러개가 될 수 도 있다.

X = tf.placeholder(tf.float32, shape=[None])

Y = tf.placeholder(tf.float32, shape=[None])

# 가설: H(x) = Wx + b

W = tf.Variable(tf.random_normal([1]), name='weight')

b = tf.Variable(tf.random_normal([1]), name='bias')

hypothesis = X * W + b

# Cost: 1/m * H(x)

cost = tf.reduce_mean(tf.square(hypothesis - Y))

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)

train = optimizer.minimize(cost)

sess = tf.Session()

sess.run(tf.global_variables_initializer())

for step in range(2001):

    cost_val, W_val, b_val, _ = sess.run([cost, W, b, train], feed_dict={X: [1, 2, 3], Y: [1, 2, 3]})

    if step % 20 == 0:

        print(step, cost_val, W_val, b_val)

// Python 2.7

$ virtualenv --system-site-packages tf_py_2.7

$ source ~/tf_py_2.7/bin/activate

(tf_py_2.7) $

// Python 3.x

$ virtualenv --system-site-packages -p python3 tf_py_3

$ source ~/tf_py_3/bin/activate

(tf_py_3) $

// Python 2.7

(tf_py_2.7) $ pip install --upgrade tensorflow

// Python 3.x

(tf_py_3) $ pip3 install --upgrade tensorflow