├── README.md ├── backup └── tech_blog_source.tgz ├── file-lock ├── file-getlock.c └── file-lock.c ├── pegasos ├── Pegasos.pdf ├── digits.zip ├── pegasos.py └── pegasos2.py ├── svm ├── blog_kernel.py ├── blog_linear.py ├── smo.py └── svm.train ├── svm_ocr ├── digits.zip ├── models │ ├── 0_a.model │ ├── 0_b.model │ ├── 1_a.model │ ├── 1_b.model │ ├── 2_a.model │ ├── 2_b.model │ ├── 3_a.model │ ├── 3_b.model │ ├── 4_a.model │ ├── 4_b.model │ ├── 5_a.model │ ├── 5_b.model │ ├── 6_a.model │ ├── 6_b.model │ ├── 7_a.model │ ├── 7_b.model │ ├── 8_a.model │ ├── 8_b.model │ ├── 9_a.model │ └── 9_b.model └── svm_ocr.py └── weibo ├── .gitignore ├── app.js ├── models ├── db.js ├── post.js └── user.js ├── package.json ├── public ├── img │ ├── glyphicons-halflings-white.png │ └── glyphicons-halflings.png ├── javascripts │ ├── bootstrap.js │ └── jquery-1.10.2.min.js └── stylesheets │ ├── bootstrap-responsive.css │ ├── bootstrap.css │ └── style.css ├── routes └── index.js ├── settings.js ├── test └── testmongo.js └── views ├── index.jade ├── index_old.jade ├── layout.jade ├── login.jade ├── posts.jade ├── reg.jade ├── say.jade └── user.jade /README.md: -------------------------------------------------------------------------------- 1 | blog_projects 2 | ============= 3 | 4 | projects for blogs 5 | 6 | ## Weibo 7 | 8 | 使用node+mongodb实现的一个简单的微博系统。参考了《Node.js开发指南》中第5章的例子。书中的例子使用的是ejs作为模板引擎，我使用的是jade。 9 | 运行方法，进入weibo目录下，运行：npm i && node app.js。 10 | -------------------------------------------------------------------------------- /backup/tech_blog_source.tgz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/liuhongjiang/blog_code/5a8c9a0c62669a17580a1a5fe133dca7bb94de06/backup/tech_blog_source.tgz -------------------------------------------------------------------------------- /file-lock/file-getlock.c: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | 6 | int getlock(int fd){ 7 | static struct flock lock; 8 | 9 | lock.l_type = F_WRLCK; 10 | lock.l_start = 0; 11 | lock.l_whence = SEEK_SET; 12 | lock.l_len = 0; 13 | lock.l_pid = getpid(); 14 | 15 | int ret = fcntl(fd, F_GETLK, &lock); 16 | printf("Return value of fcntl:%d\n",ret); 17 | if (lock.l_type == F_UNLCK){ 18 | printf("file is not locked\n"); 19 | } 20 | else { 21 | printf("file is locked\n"); 22 | } 23 | } 24 | 25 | int main(int argc, char **argv) { 26 | if (argc > 1) { 27 | int fd = open(argv[1], O_WRONLY); 28 | if(fd == -1) { 29 | printf("Unable to open the file\n"); 30 | exit(1); 31 | } 32 | getlock(fd); 33 | close(fd); 34 | 35 | fd = open("abcd", O_WRONLY | O_CREAT, S_IRUSR | S_IWUSR | S_IRGRP | S_IWGRP); 36 | if(fd == -1) { 37 | printf("Unable to open the file\n"); 38 | exit(1); 39 | } 40 | close(fd); 41 | remove(argv[1]); 42 | rename("abcd", argv[1]); 43 | printf("the file is removed and a new file is create\n"); 44 | 45 | fd = open(argv[1], O_WRONLY); 46 | if(fd == -1) { 47 | printf("Unable to open the file\n"); 48 | exit(1); 49 | } 50 | getlock(fd); 51 | 52 | // lock it 53 | fd = open(argv[1], O_WRONLY); 54 | if(fd == -1) { 55 | printf("unable to open the file\n"); 56 | exit(1); 57 | } 58 | static struct flock lock; 59 | 60 | lock.l_type = F_WRLCK; 61 | lock.l_start = 0; 62 | lock.l_whence = SEEK_SET; 63 | lock.l_len = 0; 64 | lock.l_pid = getpid(); 65 | 66 | int ret = fcntl(fd, F_SETLKW, &lock); 67 | printf("Return value of fcntl:%d\n",ret); 68 | if(ret==0) { 69 | while (1) { 70 | sleep(5); 71 | } 72 | } 73 | close(fd); 74 | } 75 | } 76 | -------------------------------------------------------------------------------- /file-lock/file-lock.c: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | 6 | int main(int argc, char **argv) { 7 | if (argc > 1) { 8 | int fd = open(argv[1], O_WRONLY); 9 | if(fd == -1) { 10 | printf("unable to open the file\n"); 11 | exit(1); 12 | } 13 | static struct flock lock; 14 | 15 | lock.l_type = F_WRLCK; 16 | lock.l_start = 0; 17 | lock.l_whence = SEEK_SET; 18 | lock.l_len = 0; 19 | lock.l_pid = getpid(); 20 | 21 | int ret = fcntl(fd, F_SETLKW, &lock); 22 | printf("Return value of fcntl:%d\n",ret); 23 | if(ret==0) { 24 | while (1) { 25 | sleep(5); 26 | } 27 | } 28 | } 29 | } 30 | -------------------------------------------------------------------------------- /pegasos/Pegasos.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/liuhongjiang/blog_code/5a8c9a0c62669a17580a1a5fe133dca7bb94de06/pegasos/Pegasos.pdf -------------------------------------------------------------------------------- /pegasos/digits.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/liuhongjiang/blog_code/5a8c9a0c62669a17580a1a5fe133dca7bb94de06/pegasos/digits.zip -------------------------------------------------------------------------------- /pegasos/pegasos.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | # Pegasos implemented in Python 5 | 6 | import os 7 | import sys 8 | import math 9 | 10 | G_WEIGHT = [] 11 | 12 | def parse_image(path): 13 | img_map = [] 14 | fp = open(path, "r") 15 | for line in fp: 16 | line = line[:-2] 17 | for ch in line: 18 | img_map.append(int(ch)) 19 | return img_map 20 | 21 | def predict(model, data): 22 | ret = 0 23 | for i in range(32*32): 24 | ret += model[i]*data[i] 25 | return ret 26 | 27 | def train_one_model(data, label, sampleNum, modelNum): 28 | pvalue = predict(G_WEIGHT[modelNum], data) 29 | # the hinge loss 30 | if pvalue * label >= 1: 31 | return 32 | 33 | # update model 34 | lambd = 0.5 35 | new_weight = [] 36 | for i in range(32*32): 37 | # pegasos 38 | a = G_WEIGHT[modelNum][i] * ( 1 - 1.0/sampleNum) + (1.0 / (lambd * sampleNum))*label*data[i] 39 | new_weight.append(a) 40 | 41 | # projection 42 | norm2 = 0 43 | for i in range(32*32): 44 | norm2 += math.pow(new_weight[i], 2) 45 | norm2 = math.sqrt(norm2) 46 | if norm2 > (1/math.sqrt(lambd)): 47 | for i in range(32*32): 48 | G_WEIGHT[modelNum][i] = new_weight[i]/(norm2 * math.sqrt(lambd)) 49 | else: 50 | G_WEIGHT[modelNum] = new_weight 51 | 52 | def train_one_sample(data, num, sampleNum): 53 | for modelNum in range(10): 54 | label = -1 55 | if num == modelNum: 56 | label = 1 57 | train_one_model(data, label, sampleNum, modelNum) 58 | 59 | if __name__== "__main__": 60 | for i in range(10): 61 | G_WEIGHT.append([]) 62 | for j in range(32 * 32): 63 | G_WEIGHT[i].append(0) 64 | 65 | dirpath = "./trainingDigits/" 66 | files = os.listdir(dirpath) 67 | sampleNum = 0 68 | for file in files: 69 | print "training:", file 70 | data = parse_image(dirpath + file) 71 | num = int(file[0]) 72 | sampleNum += 1 73 | train_one_sample(data, num, sampleNum) 74 | 75 | # test 76 | testdir = "./testDigits/" 77 | files = os.listdir(testdir) 78 | right = 0 79 | wrong = 0 80 | can_not_classify = 0 81 | total = 0 82 | for file in files: 83 | total += 1 84 | data = parse_image(testdir + file) 85 | print "testing:", file 86 | num = int(file[0]) 87 | classify_failed = True 88 | for i in range(10): 89 | pvalue = predict(G_WEIGHT[i], data) 90 | if pvalue > 0: 91 | classify_failed = False 92 | print i, "prdict:", 1 93 | if i == num: 94 | right += 1 95 | else: 96 | wrong += 1 97 | else: 98 | print i, "prdict:", -1 99 | if classify_failed: 100 | can_not_classify += 1 101 | 102 | print "right=", right 103 | print "wrong=", wrong 104 | print "can_not_classify=", can_not_classify 105 | print "total=", total 106 | 107 | -------------------------------------------------------------------------------- /pegasos/pegasos2.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | # Pegasos implemented in Python 5 | # an elegant version of pegasos.py using numpy 6 | 7 | import os 8 | import sys 9 | import math 10 | import numpy as np 11 | 12 | G_WEIGHT = np.zeros((10,32*32), dtype=float) 13 | 14 | def parse_image(path): 15 | img_map = np.zeros((32,32), dtype = float) 16 | line_no = 0 17 | fp = open(path, "r") 18 | for line in fp: 19 | line = line[:-2] 20 | img_map[line_no]=[int(x) for x in line] 21 | line_no += 1 22 | return img_map.ravel() 23 | 24 | def predict(model, data): 25 | return np.inner(model, data) 26 | 27 | def train_one_model(data, label, sampleNum, modelNum): 28 | pvalue = predict(G_WEIGHT[modelNum], data) 29 | # the hinge loss 30 | if pvalue * label >= 1: return 31 | 32 | # update model 33 | lambd = 0.5 34 | new_weight = G_WEIGHT[modelNum] * ( 1 - 1.0/sampleNum) + (1.0 / (lambd * sampleNum))*label*data 35 | 36 | # projection 37 | norm2 = np.linalg.norm(new_weight) 38 | if norm2 > (1/math.sqrt(lambd)): 39 | G_WEIGHT[modelNum] = new_weight/(norm2 * math.sqrt(lambd)) 40 | else: 41 | G_WEIGHT[modelNum] = new_weight 42 | 43 | def train_one_sample(data, num, sampleNum): 44 | for modelNum in range(10): 45 | label = -1 46 | if num == modelNum: 47 | label = 1 48 | train_one_model(data, label, sampleNum, modelNum) 49 | 50 | if __name__== "__main__": 51 | dirpath = "./trainingDigits/" 52 | files = os.listdir(dirpath) 53 | sampleNum = 0 54 | for file in files: 55 | print "training:", file 56 | data = parse_image(dirpath + file) 57 | num = int(file[0]) 58 | sampleNum += 1 59 | train_one_sample(data, num, sampleNum) 60 | 61 | # test 62 | testdir = "./testDigits/" 63 | files = os.listdir(testdir) 64 | right = 0 65 | wrong = 0 66 | can_not_classify = 0 67 | total = 0 68 | for file in files: 69 | total += 1 70 | data = parse_image(testdir + file) 71 | print "testing:", file 72 | num = int(file[0]) 73 | classify_failed = True 74 | for i in range(10): 75 | pvalue = predict(G_WEIGHT[i], data) 76 | if pvalue > 0: 77 | classify_failed = False 78 | print i, "prdict:", 1 79 | if i == num: 80 | right += 1 81 | else: 82 | wrong += 1 83 | else: 84 | print i, "prdict:", -1 85 | if classify_failed: 86 | can_not_classify += 1 87 | 88 | print "right=", right 89 | print "wrong=", wrong 90 | print "can_not_classify=", can_not_classify 91 | print "total=", total 92 | 93 | -------------------------------------------------------------------------------- /svm/blog_kernel.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | # 生成展示核技巧的图片 5 | # generate the picture for demonstrating the kernel method. 6 | 7 | import sys 8 | import random 9 | import matplotlib.pyplot as plt 10 | import pylab 11 | import numpy as np 12 | import math 13 | 14 | if __name__ == "__main__": 15 | samples = [] 16 | 17 | data_fn = "svm.kernel" 18 | 19 | fn = open(data_fn, "w") 20 | for i in range(0, 50): 21 | x1 = random.randint(-30, 30) 22 | x2 = random.randint(-30, 30) 23 | label = x1*x1 + x2*x2 - 23*23 24 | if label < 50 and label > -30: 25 | i -= 1 26 | continue 27 | if math.fabs(x1) > 28 or math.fabs(x2) > 28: 28 | i -= 1 29 | continue 30 | if label > 0: 31 | label = 1 32 | else: 33 | label = -1 34 | print x1, x2, label 35 | fn.write("%d\t%d\t%d\n" %(x1, x2, label)) 36 | samples.append((x1, x2, label)) 37 | 38 | fn.close() 39 | 40 | fig = plt.figure() 41 | ax = fig.add_subplot(1,2,1) 42 | plt.title("Data") 43 | ftrain = open(data_fn, "r") 44 | for line in ftrain: 45 | line = line[:-1] 46 | sam = line.split("\t") 47 | if int(sam[2]) > 0: 48 | ax.plot(sam[0],sam[1], 'or') 49 | else: 50 | ax.plot(sam[0],sam[1], 'og') 51 | ftrain.close() 52 | 53 | cir = pylab.Circle((0,0), radius=23, fc='none') 54 | pylab.gca().add_patch(cir) 55 | ax.spines['left'].set_position('center') 56 | ax.spines['right'].set_color('none') 57 | ax.spines['bottom'].set_position('center') 58 | ax.spines['top'].set_color('none') 59 | ax.spines['left'].set_smart_bounds(True) 60 | ax.spines['bottom'].set_smart_bounds(True) 61 | ax.xaxis.set_ticks_position('bottom') 62 | pylab.text(-2, -35, "${x_1}$", {'color':'b','fontsize' : 20}) 63 | pylab.text(-37, 0, "${x_2}$", {'color':'b','fontsize' : 20}) 64 | ax.yaxis.set_ticks_position('left') 65 | 66 | ax = fig.add_subplot(1,2,2) 67 | ftrain = open(data_fn, "r") 68 | for line in ftrain: 69 | line = line[:-1] 70 | sam = line.split("\t") 71 | x1 = int(sam[0]) 72 | x2 = int(sam[1]) 73 | if int(sam[2]) > 0: 74 | ax.plot(x1*x1,x2*x2, 'or') 75 | else: 76 | ax.plot(x1*x1,x2*x2, 'og') 77 | ftrain.close() 78 | p1=[0,23*23] 79 | p2 = [23*23,0] 80 | pylab.gca().plot(p1,p2,'b') 81 | pylab.gca().set_xlabel("${x_1^2}$",{'color':'b','fontsize' : 20}) 82 | pylab.text(-100, 500, "${x_2^2}$",{'color':'b','fontsize' : 20}) 83 | pylab.gca().set_xlim(0,1000) 84 | pylab.gca().set_ylim(0,1000) 85 | plt.title("After $x_i^2$") 86 | plt.show() 87 | 88 | 89 | -------------------------------------------------------------------------------- /svm/blog_linear.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | # 生成博客中的线性支持向量机的相关图片 5 | # this script generate the pictures for linear svm. 6 | 7 | import sys 8 | import random 9 | import matplotlib.pyplot as plt 10 | 11 | if __name__ == "__main__": 12 | w = -0.6 13 | b = 80 14 | 15 | # 控制产生的样本点是否能够被一条直线划分开 16 | # control the samples can be separated by a line 17 | separable = True 18 | 19 | data_fn = "svm.train_new" 20 | 21 | samples = [] 22 | negative = 0 23 | 24 | fn = open(data_fn, "w") 25 | for i in range(0, 50): 26 | x1 = random.randint(20, 80) 27 | x2 = random.randint(20, 80) 28 | label = x2 - (x1 * w + 80) 29 | if separable == True and label < 3 and label > -3: 30 | i -= 1 31 | continue 32 | if label > 0: 33 | if label < 10 and random.randint(0,100) < 30 and separable == False: 34 | label = -1 35 | else: 36 | label = 1 37 | negative += 1 38 | else: 39 | if label > -10 and random.randint(0,100) < 30 and separable == False: 40 | label = 1 41 | else: 42 | label = -1 43 | negative += 1 44 | print x1, x2, label 45 | fn.write("%d\t%d\t%d\n" %(x1, x2, label)) 46 | samples.append((x1, x2, label)) 47 | 48 | fn.close() 49 | print negative 50 | 51 | plt.xlabel(u"x1") 52 | plt.xlim(0, 100) 53 | plt.ylabel(u"x2") 54 | plt.ylim(0, 100) 55 | plt.title("SVM - Train Data") 56 | ftrain = open(data_fn, "r") 57 | for line in ftrain: 58 | line = line[:-1] 59 | sam = line.split("\t") 60 | if int(sam[2]) > 0: 61 | plt.plot(sam[0],sam[1], 'or') 62 | else: 63 | plt.plot(sam[0],sam[1], 'og') 64 | 65 | lp_x1 = [10, 90] 66 | lp_x2 = [] 67 | lp_x2up = [] 68 | lp_x2down = [] 69 | w = w 70 | b = b 71 | for x1 in lp_x1: 72 | lp_x2.append(w * x1 + b) 73 | lp_x2up.append((w - 0.07) * x1 + b + 4) 74 | lp_x2down.append((w + 0.1) * x1 + b - 6) 75 | plt.plot(lp_x1, lp_x2, 'b') 76 | # plt.plot(lp_x1, lp_x2up, 'r') 77 | # plt.plot(lp_x1, lp_x2down, 'y') 78 | plt.show() 79 | 80 | 81 | -------------------------------------------------------------------------------- /svm/smo.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | # SMO的一个简单实现 5 | # implement SMO 6 | 7 | import sys 8 | import math 9 | import matplotlib.pyplot as plt 10 | 11 | samples = [] 12 | labels = [] 13 | class svm_params: 14 | def __init__(self): 15 | self.a = [] 16 | self.b = 0 17 | 18 | params = svm_params() 19 | e_dict = [] 20 | 21 | #train_data = "svm.train_new" 22 | train_data = "svm.train" 23 | 24 | def loaddata(): 25 | fn = open(train_data,"r") 26 | for line in fn: 27 | line = line[:-1] 28 | vlist = line.split("\t") 29 | samples.append((int(vlist[0]), int(vlist[1]))) 30 | labels.append(int(vlist[2])) 31 | params.a.append(0.0) 32 | fn.close() 33 | 34 | # linear 35 | def kernel(j, i): 36 | ret = 0.0 37 | for idx in range(len(samples[j])): 38 | ret += samples[j][idx] * samples[i][idx] 39 | return ret 40 | 41 | def predict_real_diff(i): 42 | diff = 0.0 43 | for j in range(len(samples)): 44 | diff += params.a[j] * labels[j] * kernel(j,i) 45 | diff = diff + params.b - labels[i] 46 | return diff 47 | 48 | def init_e_dict(): 49 | for i in range(len(params.a)): 50 | e_dict.append(predict_real_diff(i)) 51 | 52 | def update_e_dict(): 53 | for i in range(len(params.a)): 54 | e_dict[i] = predict_real_diff(i) 55 | 56 | def train(tolerance, times, C): 57 | time = 0 58 | init_e_dict() 59 | updated = True 60 | while time < times and updated: 61 | updated = False 62 | time += 1 63 | for i in range(len(params.a)): 64 | ai = params.a[i] 65 | Ei = e_dict[i] 66 | # 违反KKT 67 | # agaist the KKT 68 | if (labels[i] * Ei < -tolerance and ai < C) or (labels[i] * Ei > tolerance and ai > 0): 69 | for j in range(len(params.a)): 70 | if j == i: continue 71 | eta = kernel(i, i) + kernel(j, j) - 2 * kernel(i, j) 72 | if eta <= 0: 73 | continue 74 | new_aj = params.a[j] + labels[j] * (e_dict[i] - e_dict[j]) / eta 75 | L = 0.0 76 | H = 0.0 77 | if labels[i] == labels[j]: 78 | L = max(0, params.a[j] + params.a[i] - C) 79 | H = min(C, params.a[j] + params.a[i]) 80 | else: 81 | L = max(0, params.a[j] - params.a[i]) 82 | H = min(C, C + params.a[j] - params.a[i]) 83 | if new_aj > H: 84 | new_aj = H 85 | if new_aj < L: 86 | new_aj = L 87 | # 《统计学习方法》公式7.109（下同） 88 | # formula 7.109 89 | new_ai = params.a[i] + labels[i] * labels[j] * (params.a[j] - new_aj) 90 | 91 | # 第二个变量下降是否达到最小步长 92 | # decline enough for new_aj 93 | if abs(params.a[j] - new_aj) < 0.001: 94 | print "j = %d, is not moving enough" % j 95 | continue 96 | 97 | # formula 7.115 98 | new_b1 = params.b - e_dict[i] - labels[i]*kernel(i,i)*(new_ai-params.a[i]) - labels[j]*kernel(j,i)*(new_aj-params.a[j]) 99 | # formula 7.116 100 | new_b2 = params.b - e_dict[j] - labels[i]*kernel(i,j)*(new_ai-params.a[i]) - labels[j]*kernel(j,j)*(new_aj-params.a[j]) 101 | if new_ai > 0 and new_ai < C: new_b = new_b1 102 | elif new_aj > 0 and new_aj < C: new_b = new_b2 103 | else: new_b = (new_b1 + new_b2) / 2.0 104 | 105 | params.a[i] = new_ai 106 | params.a[j] = new_aj 107 | params.b = new_b 108 | update_e_dict() 109 | updated = True 110 | print "iterate: %d, changepair: i: %d, j:%d" %(time, i, j) 111 | 112 | def draw(tolerance, C): 113 | plt.xlabel(u"x1") 114 | plt.xlim(0, 100) 115 | plt.ylabel(u"x2") 116 | plt.ylim(0, 100) 117 | plt.title("SVM - %s, tolerance %f, C %f" % (train_data, tolerance, C)) 118 | ftrain = open(train_data, "r") 119 | for line in ftrain: 120 | line = line[:-1] 121 | sam = line.split("\t") 122 | if int(sam[2]) > 0: 123 | plt.plot(sam[0],sam[1], 'or') 124 | else: 125 | plt.plot(sam[0],sam[1], 'og') 126 | 127 | w1 = 0.0 128 | w2 = 0.0 129 | for i in range(len(labels)): 130 | w1 += params.a[i] * labels[i] * samples[i][0] 131 | w2 += params.a[i] * labels[i] * samples[i][1] 132 | w = - w1 / w2 133 | 134 | b = - params.b / w2 135 | r = 1 / w2 136 | 137 | lp_x1 = [10, 90] 138 | lp_x2 = [] 139 | lp_x2up = [] 140 | lp_x2down = [] 141 | for x1 in lp_x1: 142 | lp_x2.append(w * x1 + b) 143 | lp_x2up.append(w * x1 + b + r) 144 | lp_x2down.append(w * x1 + b - r) 145 | plt.plot(lp_x1, lp_x2, 'b') 146 | plt.plot(lp_x1, lp_x2up, 'b--') 147 | plt.plot(lp_x1, lp_x2down, 'b--') 148 | plt.show() 149 | 150 | if __name__ == "__main__": 151 | loaddata() 152 | print samples 153 | print labels 154 | # 惩罚系数 155 | # penalty for mis classify 156 | C = 10 157 | # 计算精度 158 | # computational accuracy 159 | tolerance = 0.0001 160 | train(tolerance, 100, C) 161 | print "a = ", params.a 162 | print "b = ", params.b 163 | support = [] 164 | for i in range(len(params.a)): 165 | if params.a[i] > 0 and params.a[i] < C: 166 | support.append(samples[i]) 167 | print "support vector = ", support 168 | draw(tolerance, C) 169 | -------------------------------------------------------------------------------- /svm/svm.train: 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https://raw.githubusercontent.com/liuhongjiang/blog_code/5a8c9a0c62669a17580a1a5fe133dca7bb94de06/svm_ocr/digits.zip -------------------------------------------------------------------------------- /svm_ocr/models/0_a.model: -------------------------------------------------------------------------------- 1 | 0.0 2 | 0 3 | 0 4 | 0 5 | 0.427506533584 6 | 0 7 | 0 8 | 0 9 | 0 10 | -6.93889390391e-18 11 | 0 12 | 0 13 | 0.0687027727794 14 | 0 15 | 0 16 | 0 17 | 0 18 | 1.38777878078e-17 19 | 0 20 | 0 21 | 0 22 | 0.0 23 | 0 24 | 0 25 | 0 26 | 0 27 | 0 28 | 0 29 | 0 30 | 0 31 | 0 32 | 0 33 | 0 34 | 0 35 | 0 36 | 0 37 | 0 38 | 0 39 | 0 40 | 0 41 | 0 42 | 0.385802530143 43 | 0 44 | 0 45 | 0.151185952884 46 | 0 47 | 0 48 | 0 49 | 0 50 | 0 51 | 0 52 | 0 53 | 0 54 | 0 55 | 0 56 | 0 57 | 0.0 58 | 0 59 | 0 60 | 0 61 | 0 62 | 0 63 | 0 64 | 0 65 | 0 66 | 0 67 | 0 68 | 0 69 | 0 70 | 0.27726758784 71 | 0 72 | 0 73 | 0 74 | 0 75 | 0 76 | 0 77 | 0 78 | 0 79 | 0 80 | 0 81 | 0 82 | 0 83 | 0 84 | 0 85 | 0 86 | 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| 0 591 | 0 592 | 0 593 | 0.138501025518 594 | 0 595 | 0 596 | 0 597 | 0 598 | 0 599 | 0 600 | 0 601 | 0 602 | 0 603 | 0 604 | 1.54143183369 605 | 0.213470909173 606 | 0 607 | 0 608 | 0 609 | 0 610 | 0 611 | 0 612 | 0 613 | 0 614 | 0 615 | 1.30204859244 616 | 0 617 | 0 618 | 0 619 | 0 620 | 0 621 | 1.21430643318e-17 622 | 0 623 | 0 624 | 0 625 | 0 626 | 0 627 | 0 628 | 3.42045192771 629 | 0 630 | 0 631 | 0 632 | 0 633 | 0 634 | 0 635 | 0 636 | 0 637 | 0 638 | 0.108976650698 639 | 0 640 | 0 641 | 0 642 | 0 643 | 0 644 | 0 645 | 0.719633164574 646 | 0 647 | 0 648 | 0 649 | 0 650 | 0 651 | 0.420041542546 652 | 0.415993665358 653 | 0 654 | 0 655 | 0 656 | 0 657 | 0 658 | 0 659 | 0 660 | 0 661 | 0 662 | 0.339143212718 663 | 0 664 | 0 665 | 0 666 | 0 667 | 1.22965686179 668 | 0 669 | 0 670 | 0 671 | 0 672 | 0 673 | 0 674 | 0 675 | 0 676 | 0 677 | 0 678 | 0 679 | 5.95657818416e-05 680 | 0 681 | 0 682 | 0 683 | 0 684 | 0 685 | 0 686 | 0 687 | 0 688 | 0 689 | 0 690 | 0.452606424681 691 | 0.98869638244 692 | 0 693 | 0 694 | 0 695 | 0 696 | 0 697 | 0 698 | 0 699 | 0 700 | 0 701 | 0 702 | 0 703 | 0 704 | 0 705 | 0 706 | 0 707 | 0 708 | 0 709 | 0 710 | 0 711 | 0 712 | 0 713 | 0 714 | 0 715 | 0 716 | 0 717 | 0 718 | 0 719 | 0 720 | 0 721 | 0 722 | 0 723 | 0 724 | 0 725 | 0 726 | 0 727 | 0 728 | 0 729 | 0 730 | 0 731 | 0 732 | 0 733 | 0 734 | 0 735 | 0 736 | 0 737 | 0 738 | 0 739 | 0 740 | 0 741 | 0 742 | 0 743 | 0.0238435811526 744 | 0.844819829345 745 | 0 746 | 0 747 | 0 748 | 0.729462565092 749 | 0 750 | 0.474202662576 751 | 0 752 | 0 753 | 0.885562760316 754 | 0 755 | 0.0661429498532 756 | 0 757 | 0 758 | 0 759 | 0 760 | 0 761 | 0 762 | 0 763 | 0 764 | 0 765 | 0 766 | 0 767 | 0.409345027006 768 | 0 769 | 0 770 | 0 771 | 0 772 | 0 773 | 0 774 | 0 775 | 0 776 | 0 777 | 0 778 | 0 779 | 0 780 | 0 781 | 0 782 | 0 783 | 0.645301338477 784 | 0 785 | 0 786 | 0 787 | 0 788 | 0 789 | 0 790 | 0.976404534599 791 | 0.421153976693 792 | 0 793 | 3.01163200446 794 | 0 795 | 0 796 | 0 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0.942064489154 899 | 0.396070582032 900 | 0 901 | 0 902 | 0 903 | 0 904 | 0 905 | 0 906 | 0 907 | 0 908 | 0 909 | 0 910 | 0 911 | 0 912 | 0 913 | 0 914 | 1.77887742945 915 | 0 916 | 0 917 | 0 918 | 0 919 | 0 920 | 0 921 | 0 922 | 0 923 | 0 924 | 0 925 | 0 926 | 0 927 | 0.0899450944252 928 | 0 929 | 0.0773028349392 930 | 0 931 | 0 932 | 0 933 | 0 934 | 1.34235351023 935 | 0 936 | 0 937 | 0 938 | 0.488070058379 939 | 0 940 | 0.0640255466486 941 | 0.240670706626 942 | 0.970911790995 943 | 0 944 | 1.29751847224 945 | 0 946 | 0 947 | 1.14607159154 948 | 0 949 | 0 950 | 0 951 | 0 952 | 0 953 | 0 954 | 0 955 | 0 956 | 0 957 | 0 958 | 0 959 | 0 960 | 0 961 | 1.74917411064 962 | 0 963 | 0 964 | 0.850901452518 965 | 0 966 | 1.77076603206 967 | 0 968 | 0.289305368611 969 | 0 970 | 0 971 | 0 972 | 0 973 | 0 974 | 0 975 | 0 976 | 0 977 | 0 978 | 0 979 | 0 980 | 0 981 | 0 982 | 0.225466845024 983 | 0 984 | 0 985 | 0 986 | 0 987 | 0 988 | 0 989 | 0 990 | 0 991 | 1.30384751328 992 | 0 993 | 0.451036901145 994 | 0 995 | 0.0858869684527 996 | 0 997 | 0 998 | 0 999 | 0.322654380917 1000 | 0.0427665885697 1001 | 0 1002 | 0 1003 | 0 1004 | 0.436961023662 1005 | 0 1006 | 0 1007 | 0 1008 | 0 1009 | 0 1010 | 0 1011 | 0 1012 | 0 1013 | 0 1014 | 0 1015 | 0 1016 | 0 1017 | 0 1018 | 0 1019 | 0 1020 | 0 1021 | 0 1022 | 0 1023 | 0 1024 | 1.41969316974 1025 | 0 1026 | 0 1027 | 0.285746236732 1028 | 0 1029 | 0 1030 | 0 1031 | 0 1032 | 0 1033 | 0 1034 | 0 1035 | 0 1036 | 0 1037 | 0 1038 | 0 1039 | 0 1040 | 0 1041 | 0 1042 | 0 1043 | 0 1044 | 0 1045 | 0 1046 | 0 1047 | 0 1048 | 0 1049 | 0 1050 | 0 1051 | 0 1052 | 0 1053 | 0.301649543903 1054 | 0 1055 | 0 1056 | 0 1057 | 1.27500105973 1058 | 0 1059 | 0 1060 | 0.245612976956 1061 | 0 1062 | 0 1063 | 0 1064 | 0 1065 | 0 1066 | 0 1067 | 0 1068 | 0 1069 | 0 1070 | 0.0778433883952 1071 | 0.980675677978 1072 | 0 1073 | 0 1074 | 0 1075 | 0 1076 | 0 1077 | 0 1078 | 0 1079 | 0 1080 | 0 1081 | 0 1082 | 0.782407687972 1083 | 0 1084 | 0.138444801261 1085 | 0 1086 | 0 1087 | 0 1088 | 1.33447345478 1089 | 0 1090 | 0.528254126106 1091 | 0 1092 | 0 1093 | 0 1094 | 0 1095 | 0 1096 | 0.174129077203 1097 | 0.250374577327 1098 | 0 1099 | 0 1100 | 0 1101 | 0 1102 | 0 1103 | 0 1104 | 0 1105 | 0 1106 | 0 1107 | 0 1108 | 0 1109 | 0 1110 | 0.478789095821 1111 | 0 1112 | 0 1113 | 0 1114 | 0 1115 | 0 1116 | 0 1117 | 0 1118 | 0 1119 | 0 1120 | 0 1121 | 0 1122 | 0 1123 | 0 1124 | 0 1125 | 0.878525398471 1126 | 0 1127 | 0 1128 | 0 1129 | 0 1130 | 0.0280930866584 1131 | 0 1132 | 0 1133 | 0 1134 | 0 1135 | 0 1136 | 0 1137 | 0 1138 | 0 1139 | 0 1140 | 0.0463673759014 1141 | 1.80323298976 1142 | 0 1143 | 0 1144 | 0 1145 | 0.230401885444 1146 | 0 1147 | 0 1148 | 0 1149 | 0 1150 | 0 1151 | 0 1152 | 0 1153 | 0 1154 | 0.110677933474 1155 | 0 1156 | 0 1157 | 0.383109409984 1158 | 0 1159 | 0 1160 | 0 1161 | 0.298387007616 1162 | 0 1163 | 0 1164 | 0.54542985912 1165 | 0 1166 | 0 1167 | 0.998705263959 1168 | 0 1169 | 0 1170 | 0 1171 | 0.968284193659 1172 | 0.0600573272863 1173 | 0 1174 | 0 1175 | 0 1176 | 0 1177 | 0 1178 | 0 1179 | 0 1180 | 0 1181 | 0.242240981191 1182 | 0 1183 | 0 1184 | 0.360203341332 1185 | 0 1186 | 0 1187 | 0 1188 | 0 1189 | 0 1190 | 0.583080867216 1191 | 0 1192 | 0.00206981696167 1193 | 0 1194 | 0 1195 | 0 1196 | 1.25199944966 1197 | 0 1198 | 0.354763676867 1199 | 0 1200 | 0 1201 | 0 1202 | 0 1203 | 0 1204 | 0 1205 | 0 1206 | 0 1207 | 0 1208 | 0 1209 | 0 1210 | 0 1211 | 0 1212 | 1.21923965446 1213 | 0 1214 | 0 1215 | 0.0164077074254 1216 | 0 1217 | 0 1218 | 0.0376948159099 1219 | 0 1220 | 0.150310523007 1221 | 0 1222 | 0 1223 | 0.0358377872111 1224 | 0 1225 | 0 1226 | 0 1227 | 0 1228 | 0 1229 | 0 1230 | 0.653846438419 1231 | 0.166425254777 1232 | 0 1233 | 0 1234 | 0 1235 | 2.02687016783 1236 | 0.345382786124 1237 | 0 1238 | 0 1239 | 0.487330270295 1240 | 0 1241 | 0 1242 | 0 1243 | 0 1244 | 0.0611601437774 1245 | 0 1246 | 0 1247 | 0 1248 | 0 1249 | 0 1250 | 0 1251 | 0 1252 | 0 1253 | 0 1254 | 0 1255 | 0 1256 | 0.176310541722 1257 | 0.0749486207565 1258 | 0 1259 | 0 1260 | 0 1261 | 0 1262 | 0 1263 | 2.69652589166 1264 | 0 1265 | 0 1266 | 0 1267 | 0 1268 | 0 1269 | 0 1270 | 0 1271 | 0 1272 | 0 1273 | 0 1274 | 0 1275 | 0 1276 | 0 1277 | 0.356401794844 1278 | 0 1279 | 0 1280 | 0 1281 | 0.0387470569403 1282 | 0 1283 | 0 1284 | 0 1285 | 0 1286 | 0 1287 | 0 1288 | 0 1289 | 0 1290 | 0 1291 | 0 1292 | 0 1293 | 0 1294 | 0 1295 | 0 1296 | 0.00231007593235 1297 | 0 1298 | 0 1299 | 0 1300 | 0 1301 | 0 1302 | 0 1303 | 0.626388658201 1304 | 0 1305 | 0 1306 | 0 1307 | 0 1308 | 0 1309 | 0 1310 | 0 1311 | 0 1312 | 0 1313 | 0 1314 | 0 1315 | 0 1316 | 0.952404300081 1317 | 0 1318 | 0 1319 | 0 1320 | 0 1321 | 0 1322 | 0 1323 | 0 1324 | 0 1325 | 0 1326 | 0 1327 | 0 1328 | 0 1329 | 0 1330 | 0.286595627008 1331 | 0 1332 | 0 1333 | 3.9616058003 1334 | 0 1335 | 0 1336 | 0 1337 | 0 1338 | 0.489386551941 1339 | 0 1340 | 0 1341 | 0 1342 | 0.916030414317 1343 | 0.106707200503 1344 | 0 1345 | 0 1346 | 0 1347 | 0 1348 | 0 1349 | 0 1350 | 0 1351 | 0 1352 | 0.0113217401514 1353 | 0 1354 | 0 1355 | 0 1356 | 0 1357 | 0 1358 | 0 1359 | 0.335469895118 1360 | 0 1361 | 0 1362 | 0 1363 | 0 1364 | 0 1365 | 0 1366 | 0 1367 | 0 1368 | 0 1369 | 0 1370 | 0 1371 | 0 1372 | 0 1373 | 0 1374 | 0 1375 | 0 1376 | 0 1377 | 0 1378 | 0 1379 | 0 1380 | 0 1381 | 0 1382 | 0.0437573656816 1383 | 0 1384 | 0 1385 | 0.0808177466526 1386 | 0 1387 | 0 1388 | 0 1389 | 0 1390 | 0.089165337666 1391 | 0 1392 | 0.204608139256 1393 | 0.416960815792 1394 | 0 1395 | 0 1396 | 0 1397 | 0 1398 | 0 1399 | 0 1400 | 0 1401 | 0 1402 | 0.092717942726 1403 | 0.914190629362 1404 | 0 1405 | 0 1406 | 0 1407 | 0 1408 | 0 1409 | 0 1410 | 0 1411 | 0 1412 | 0 1413 | 0 1414 | 0 1415 | 0 1416 | 0.186317004343 1417 | 0 1418 | 0 1419 | 0 1420 | 1.65091041143 1421 | 0 1422 | 0 1423 | 0 1424 | 0 1425 | 0 1426 | 0 1427 | 0 1428 | 0.764018347181 1429 | 0 1430 | 0.167513081196 1431 | 0 1432 | 0 1433 | 0 1434 | 0 1435 | 0 1436 | 0 1437 | 0 1438 | 0 1439 | 0 1440 | 0 1441 | 0.431714069788 1442 | 0 1443 | 0 1444 | 0 1445 | 0 1446 | 0 1447 | 0 1448 | 0 1449 | 0 1450 | 0 1451 | 0 1452 | 0 1453 | 0 1454 | 0 1455 | 0 1456 | 0 1457 | 0 1458 | 0 1459 | 0 1460 | 0 1461 | 0 1462 | 0 1463 | 0 1464 | 0 1465 | 0 1466 | 0 1467 | 0 1468 | 1.57418043915 1469 | 0 1470 | 0.207487225085 1471 | 0.092859636922 1472 | 0 1473 | 0.262334688516 1474 | 0.911866791461 1475 | 0 1476 | 0 1477 | 0 1478 | 0 1479 | 0 1480 | 0 1481 | 0 1482 | 0 1483 | 0 1484 | 0 1485 | 0 1486 | 0 1487 | 0 1488 | 0 1489 | 0 1490 | 0 1491 | 0 1492 | 0 1493 | 0 1494 | 0 1495 | 0 1496 | 0 1497 | 0.469818423257 1498 | 0 1499 | 0 1500 | 0 1501 | 0 1502 | 0 1503 | 0 1504 | 0.696254232474 1505 | 0 1506 | 0 1507 | 0 1508 | 0 1509 | 0 1510 | 0 1511 | 0 1512 | 0 1513 | 0 1514 | 0 1515 | 0 1516 | 0 1517 | 0.233756732339 1518 | 0.511692408606 1519 | 0.125000472427 1520 | 0 1521 | 0 1522 | 0.192627877622 1523 | 0 1524 | 0 1525 | 0 1526 | 0 1527 | 0 1528 | 0 1529 | 3.12631268155 1530 | 1.09724200232 1531 | 0 1532 | 0 1533 | 0 1534 | 0 1535 | 0 1536 | 0.72949735359 1537 | 0 1538 | 0.709013091754 1539 | 0 1540 | 0 1541 | 0 1542 | 0 1543 | 0 1544 | 0 1545 | 0 1546 | 0 1547 | 0 1548 | 0 1549 | 0 1550 | 0 1551 | 0 1552 | 0 1553 | 0 1554 | 0 1555 | 0 1556 | 0 1557 | 0 1558 | 1.07697890771 1559 | 0 1560 | 0 1561 | 0 1562 | 0 1563 | 0 1564 | 0 1565 | 0 1566 | 0 1567 | 0 1568 | 0 1569 | 1.43524097414 1570 | 0 1571 | 0 1572 | 0 1573 | 0 1574 | 1.18764310899 1575 | 0 1576 | 0 1577 | 0 1578 | 0 1579 | 0.0207778164972 1580 | 0 1581 | 0 1582 | 0 1583 | 0 1584 | 0 1585 | 0.869451147414 1586 | 1.37172171191 1587 | 0 1588 | 0 1589 | 0 1590 | 0 1591 | 0 1592 | 0 1593 | 0 1594 | 0.107158254402 1595 | 0.424860681047 1596 | 0 1597 | 0 1598 | 0 1599 | 0 1600 | 0.5697606577 1601 | 0 1602 | 0 1603 | 0 1604 | 0.810823915111 1605 | 0 1606 | 0 1607 | 0 1608 | 0 1609 | 0 1610 | 0 1611 | 0 1612 | 0.800234926707 1613 | 0 1614 | 0 1615 | 0 1616 | 0 1617 | 0 1618 | 0 1619 | 0 1620 | 0 1621 | 0 1622 | 0 1623 | 0.192942611283 1624 | 0.244991577647 1625 | 0.0411060416478 1626 | 0.907221361305 1627 | 0 1628 | 0 1629 | 0 1630 | 0.641224688313 1631 | 0 1632 | 0 1633 | 0 1634 | 0 1635 | 1.56552064061 1636 | 0 1637 | 0 1638 | 0 1639 | 0 1640 | 0 1641 | 0 1642 | 0 1643 | 0 1644 | 0 1645 | 0.630005417913 1646 | 0 1647 | 0 1648 | 0 1649 | 1.11701949756 1650 | 0 1651 | 0 1652 | 0 1653 | 0 1654 | 0.0438939440697 1655 | 0 1656 | 0 1657 | 0 1658 | 0 1659 | 0 1660 | 0 1661 | 0 1662 | 0 1663 | 0 1664 | 0 1665 | 0 1666 | 0 1667 | 0 1668 | 0 1669 | 0 1670 | 0 1671 | 0 1672 | 0 1673 | 0 1674 | 1.72169967155 1675 | 0 1676 | 0.4002216726 1677 | 0 1678 | 0 1679 | 0 1680 | 0 1681 | 0 1682 | 0 1683 | 0 1684 | 0.398501605792 1685 | 0 1686 | 0 1687 | 0 1688 | 0.283803104409 1689 | 0 1690 | 0.084469128554 1691 | 0 1692 | 0 1693 | 0 1694 | 0 1695 | 0 1696 | 0 1697 | 0 1698 | 0.313803521121 1699 | 0.597693883776 1700 | 0 1701 | 0 1702 | 0 1703 | 0 1704 | 0 1705 | 0 1706 | 0 1707 | 0 1708 | 0 1709 | 0 1710 | 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1882 | 0 1883 | 2.55861115759 1884 | 0 1885 | 0.421364806916 1886 | 0 1887 | 0 1888 | 0 1889 | 0 1890 | 1.08180756254 1891 | 0 1892 | 4.63739150173e-05 1893 | 0 1894 | 0 1895 | 0 1896 | 0 1897 | 0 1898 | 0 1899 | 0 1900 | 0 1901 | 0 1902 | 0 1903 | 0 1904 | 0 1905 | 0 1906 | 0 1907 | 0 1908 | 0 1909 | 0 1910 | 0 1911 | 0 1912 | 0 1913 | 0 1914 | 0 1915 | 0 1916 | 0 1917 | 0 1918 | 0.143428322022 1919 | 0 1920 | 0 1921 | 0 1922 | 0 1923 | 0.621979341055 1924 | 0 1925 | 0 1926 | 0 1927 | 0 1928 | 0 1929 | 0 1930 | 0 1931 | 0 1932 | 0 1933 | 0 1934 | 0 1935 | -------------------------------------------------------------------------------- /svm_ocr/models/8_b.model: -------------------------------------------------------------------------------- 1 | -1.14122846002 -------------------------------------------------------------------------------- /svm_ocr/models/9_b.model: -------------------------------------------------------------------------------- 1 | -1.43628861393 -------------------------------------------------------------------------------- /svm_ocr/svm_ocr.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | ### ### 5 | # handwriting numbers OCR based on svm. 6 | # data from "Machine Learning in Action" chapter 02. 7 | # 基于SVM的手写数字的识别程序 8 | # 数据：采用了《Machine Learning in Action》第二章的数据 9 | ### ### 10 | 11 | import sys 12 | import os 13 | import math 14 | 15 | class model: 16 | def __init__(self): 17 | self.a = [] 18 | self.b = 0.0 19 | 20 | class GV: 21 | def __init__(self): 22 | self.samples = [] # 样本数据 23 | self.tests = [] # 测试数据 24 | self.models = [] # 训练的模型 25 | self.diff_dict = [] # 用于缓存预测知与真实y之差Ei 26 | self.cur_mno = 0 # 当前正使用或训练的模型 27 | self.cache_kernel = [] # 缓存kernel函数的计算结果 28 | self.use_linear = False # 是否使用线性核函数 29 | self.RBF_dlt = 10 # 径向基函数的宽度 30 | 31 | def init_models(self): 32 | for i in range(0, 10): 33 | m = model() 34 | for j in range(len(self.samples)): 35 | m.a.append(0) 36 | self.models.append(m) 37 | 38 | def init_cache_kernel(self): 39 | i = 0 40 | for mi in self.samples: 41 | print i 42 | self.cache_kernel.append([]) 43 | j = 0 44 | for mj in self.samples: 45 | if i > j: 46 | self.cache_kernel[i].append(self.cache_kernel[j][i]) 47 | else: 48 | self.cache_kernel[i].append(kernel(mi,mj)) 49 | j += 1 50 | i += 1 51 | 52 | class image: 53 | def __init__(self): 54 | self.data = [] 55 | self.num = 0 56 | self.label = [] 57 | self.fn = "" 58 | 59 | def printself(self): 60 | print "data" 61 | for line in self.data: 62 | print line 63 | print "num", self.num 64 | print "label", self.label[gv.cur_mno] 65 | print "fn", self.fn 66 | 67 | # global variables 68 | gv = GV() 69 | 70 | def parse_image(path): 71 | img_map = [] 72 | fp = open(path, "r") 73 | for line in fp: 74 | line = line[:-2] 75 | img_map.append(line) 76 | return img_map 77 | 78 | # load samples and tests 79 | def loaddata(dirpath, col): 80 | files = os.listdir(dirpath) 81 | for file in files: 82 | img = image() 83 | img.data = parse_image(dirpath + file) 84 | img.num = int(file[0]) 85 | img.fn = file 86 | col.append(img) 87 | 88 | def kernel(mj, mi): 89 | if gv.use_linear == True: 90 | return kernel_linear(mj,mi) 91 | else: 92 | return kernel_RBF(mj,mi) 93 | 94 | ###### 95 | # 高斯核函数 96 | ###### 97 | def kernel_RBF(mj, mi): 98 | if mj == mi: 99 | return math.exp(0) 100 | dlt = gv.RBF_dlt 101 | ret = 0.0 102 | for i in range(len(mj.data)): 103 | for j in range(len(mj.data[i])): 104 | ret += math.pow(int(mj.data[i][j]) - int(mi.data[i][j]), 2) 105 | ret = math.exp(-ret/(2*dlt*dlt)) 106 | return ret 107 | 108 | ###### 109 | # 线性 110 | ###### 111 | def kernel_linear(mj, mi): 112 | ret = 0.0 113 | for i in range(len(mj.data)): 114 | for j in range(len(mj.data[i])): 115 | ret += int(mj.data[i][j]) * int(mi.data[i][j]) 116 | return ret 117 | 118 | 119 | # g(x) 120 | def predict(m): 121 | pred = 0.0 122 | for j in range(len(gv.samples)): 123 | if gv.models[gv.cur_mno].a[j] != 0: 124 | pred += gv.models[gv.cur_mno].a[j] * gv.samples[j].label[gv.cur_mno] * kernel(gv.samples[j],m) 125 | pred += gv.models[gv.cur_mno].b 126 | return pred 127 | 128 | # the same as predict(m), only with different parmaters 129 | def predict_train(i): 130 | pred = 0.0 131 | for j in range(len(gv.samples)): 132 | if gv.models[gv.cur_mno].a[j] != 0: 133 | pred += gv.models[gv.cur_mno].a[j] * gv.samples[j].label[gv.cur_mno] * gv.cache_kernel[j][i] 134 | pred += gv.models[gv.cur_mno].b 135 | return pred 136 | 137 | # 决策函数对xi的预测值和真实值之差 138 | def predict_diff_real(i): 139 | diff = predict_train(i) 140 | diff -= gv.samples[i].label[gv.cur_mno] 141 | return diff 142 | 143 | # 优化计算Ei 144 | def predict_diff_real_optimized(idx, i, new_ai, j, new_aj, new_b): 145 | diff = (new_ai - gv.models[gv.cur_mno].a[i])* gv.samples[i].label[gv.cur_mno] * gv.cache_kernel[i][idx] 146 | diff += (new_aj - gv.models[gv.cur_mno].a[j])* gv.samples[j].label[gv.cur_mno] * gv.cache_kernel[j][idx] 147 | diff += new_b - gv.models[gv.cur_mno].b 148 | diff += gv.diff_dict[idx] 149 | return diff 150 | 151 | 152 | def init_predict_diff_real_dict(): 153 | gv.diff_dict = [] 154 | for i in range(len(gv.samples)): 155 | gv.diff_dict.append(predict_diff_real(i)) 156 | 157 | def update_diff_dict(i, new_ai, j, new_bj, new_b): 158 | for idx in range(len(gv.samples)): 159 | # 原来的函数 160 | # gv.diff_dict[idx] = predict_diff_real(idx) 161 | # 有优化后的 162 | gv.diff_dict[idx] = predict_diff_real_optimized(idx, i, new_ai, j, new_bj, new_b) 163 | 164 | def update_samples_label(num): 165 | for img in gv.samples: 166 | if img.num == num: 167 | img.label.append(1) 168 | else: 169 | img.label.append(-1) 170 | 171 | ###### 172 | # svmocr train 173 | # 基于算法SMO 174 | # T: tolerance 误差容忍度(精度) 175 | # times: 迭代次数 176 | # C: 惩罚系数 177 | # Mno: 模型序号0到9 178 | # step: aj移动的最小步长 179 | ###### 180 | def SVM_SMO_train(T, times, C, Mno, step): 181 | time = 0 182 | gv.cur_mno = Mno 183 | update_samples_label(Mno) 184 | init_predict_diff_real_dict() 185 | updated = True 186 | while time < times and updated: 187 | updated = False 188 | time += 1 189 | for i in range(len(gv.samples)): 190 | ai = gv.models[gv.cur_mno].a[i] 191 | Ei = gv.diff_dict[i] 192 | 193 | # agaist the KKT 194 | if (gv.samples[i].label[gv.cur_mno] * Ei < -T and ai < C) or (gv.samples[i].label[gv.cur_mno] * Ei > T and ai > 0): 195 | for j in range(len(gv.samples)): 196 | if j == i: continue 197 | kii = gv.cache_kernel[i][i] 198 | kjj = gv.cache_kernel[j][j] 199 | kji = kij = gv.cache_kernel[i][j] 200 | eta = kii + kjj - 2 * kij 201 | if eta <= 0: continue 202 | new_aj = gv.models[gv.cur_mno].a[j] + gv.samples[j].label[gv.cur_mno] * (gv.diff_dict[i] - gv.diff_dict[j]) / eta # f 7.106 203 | L = 0.0 204 | H = 0.0 205 | a1_old = gv.models[gv.cur_mno].a[i] 206 | a2_old = gv.models[gv.cur_mno].a[j] 207 | if gv.samples[i].label[gv.cur_mno] == gv.samples[j].label[gv.cur_mno]: 208 | L = max(0, a2_old + a1_old - C) 209 | H = min(C, a2_old + a1_old) 210 | else: 211 | L = max(0, a2_old - a1_old) 212 | H = min(C, C + a2_old - a1_old) 213 | if new_aj > H: 214 | new_aj = H 215 | if new_aj < L: 216 | new_aj = L 217 | if abs(a2_old - new_aj) < step: 218 | print "j = %d, is not moving enough" % j 219 | continue 220 | 221 | new_ai = a1_old + gv.samples[i].label[gv.cur_mno] * gv.samples[j].label[gv.cur_mno] * (a2_old - new_aj) # f 7.109 222 | new_b1 = gv.models[gv.cur_mno].b - gv.diff_dict[i] - gv.samples[i].label[gv.cur_mno] * kii * (new_ai - a1_old) - gv.samples[j].label[gv.cur_mno] * kji * (new_aj - a2_old) # f7.115 223 | new_b2 = gv.models[gv.cur_mno].b - gv.diff_dict[j] - gv.samples[i].label[gv.cur_mno]*kji*(new_ai - a1_old) - gv.samples[j].label[gv.cur_mno]*kjj*(new_aj-a2_old) # f7.116 224 | if new_ai > 0 and new_ai < C: new_b = new_b1 225 | elif new_aj > 0 and new_aj < C: new_b = new_b2 226 | else: new_b = (new_b1 + new_b2) / 2.0 227 | 228 | update_diff_dict(i, new_ai, j, new_aj, new_b) 229 | gv.models[gv.cur_mno].a[i] = new_ai 230 | gv.models[gv.cur_mno].a[j] = new_aj 231 | gv.models[gv.cur_mno].b = new_b 232 | updated = True 233 | print "iterate: %d, changepair: i: %d, j:%d" %(time, i, j) 234 | #break 235 | 236 | # 测试数据 237 | def test(): 238 | recog = 0 239 | recog_correct = 0 240 | for img in gv.tests: 241 | print "test for", img.fn 242 | for mno in range(10): 243 | gv.cur_mno = mno 244 | if predict(img) > 0: 245 | print mno 246 | print img.fn 247 | recog += 1 248 | if mno == int(img.fn[0]): 249 | recog_correct += 1 250 | break 251 | print "recog:", recog 252 | print "recog_correct:", recog_correct 253 | print "total:", len(gv.tests) 254 | 255 | def save_models(): 256 | for i in range(10): 257 | fn = open("models/" + str(i) + "_a.model", "w") 258 | for ai in gv.models[i].a: 259 | fn.write(str(ai)) 260 | fn.write('\n') 261 | fn.close() 262 | fn = open("models/" + str(i) + "_b.model", "w") 263 | fn.write(str(gv.models[i].b)) 264 | fn.close() 265 | 266 | def load_models(): 267 | for i in range(10): 268 | fn = open("models/" + str(i) + "_a.model", "r") 269 | j = 0 270 | for line in fn: 271 | gv.models[i].a[j] = float(line) 272 | j += 1 273 | fn.close() 274 | fn = open("models/" + str(i) + "_b.model", "r") 275 | gv.models[i].b = float(fn.readline()) 276 | fn.close() 277 | 278 | if __name__ == "__main__": 279 | training = True 280 | loaddata("trainingDigits/", gv.samples) 281 | loaddata("testDigits/", gv.tests) 282 | print len(gv.samples) 283 | print len(gv.tests) 284 | 285 | if training == True: 286 | gv.init_cache_kernel() 287 | gv.init_models() 288 | 289 | print "init_models done" 290 | 291 | T = 0.0001 292 | C = 10 293 | step = 0.0001 294 | gv.RBF_dlt = 8 295 | if training == True: 296 | for i in range(10): 297 | print "traning model no:", i 298 | SVM_SMO_train(T, 100, C, i, step) 299 | save_models() 300 | else: 301 | load_models() 302 | for i in range(10): 303 | update_samples_label(i) 304 | test() 305 | -------------------------------------------------------------------------------- /weibo/.gitignore: -------------------------------------------------------------------------------- 1 | node_modules 2 | -------------------------------------------------------------------------------- /weibo/app.js: -------------------------------------------------------------------------------- 1 | 2 | /** 3 | * Module dependencies. 4 | */ 5 | 6 | var express = require('express'); 7 | var routes = require('./routes'); 8 | var user = require('./models/user'); 9 | var http = require('http'); 10 | var path = require('path'); 11 | 12 | var MongoStore = require('connect-mongodb'); 13 | var settings = require('./settings'); 14 | 15 | var app = express(); 16 | 17 | // all environments 18 | // can't change the orders??? 19 | app.set('port', process.env.PORT || 3000); 20 | app.set('views', path.join(__dirname, 'views')); 21 | app.set('view engine', 'jade'); 22 | 23 | app.use(express.favicon()); 24 | app.use(express.logger('dev')); 25 | app.use(express.bodyParser()); 26 | app.use(express.methodOverride()); 27 | app.use(express.cookieParser()); 28 | 29 | app.use(express.session({ 30 | secret: settings.cookiesSecret, 31 | Store: new MongoStore({ 32 | db: settings.db 33 | }) 34 | })); 35 | 36 | 37 | app.use(function(req, res, next){ 38 | res.locals.user = req.session.user; 39 | var err = req.session.error; 40 | if (err && err.length) { 41 | res.locals.error = err; 42 | //console.log(res.locals.error); 43 | } else 44 | res.locals.error = null; 45 | 46 | var succ = req.session.success; 47 | if (succ && succ.length) { 48 | res.locals.success = succ; 49 | //console.log(res.locals.success); 50 | } else 51 | res.locals.success = null; 52 | next(); 53 | }); 54 | 55 | 56 | app.use(app.router); 57 | //app.use(express.router(routes)); 58 | app.use(express.static(path.join(__dirname, 'public'))); 59 | 60 | 61 | routes(app); 62 | 63 | // development only 64 | if ('development' == app.get('env')) { 65 | app.use(express.errorHandler()); 66 | } 67 | 68 | http.createServer(app).listen(app.get('port'), function(){ 69 | console.log('Express server listening on port ' + app.get('port')); 70 | }); 71 | -------------------------------------------------------------------------------- /weibo/models/db.js: -------------------------------------------------------------------------------- 1 | var settings = require('../settings') 2 | var Db = require('mongodb').Db 3 | var Connection = require('mongodb').Connection 4 | var Server = require('mongodb').Server 5 | 6 | module.exports = new Db(settings.db, new Server(settings.host, Connection.DEFAULT_PORT, {})); 7 | 8 | -------------------------------------------------------------------------------- /weibo/models/post.js: -------------------------------------------------------------------------------- 1 | var mongodb = require('./db') 2 | 3 | function Post(username, post, time) { 4 | this.user = username 5 | this.post = post; 6 | if (time) { 7 | this.time = time; 8 | } else { 9 | this.time = new Date(); 10 | } 11 | } 12 | 13 | module.exports = Post; 14 | 15 | Post.prototype.save = function save(callback) { 16 | var post = { 17 | user: this.user, 18 | post: this.post, 19 | time: this.time, 20 | } 21 | mongodb.open(function (err, db) { 22 | if (err) { 23 | return callback(err); 24 | } 25 | 26 | db.collection('posts', function (err, collection) { 27 | if (err) { 28 | mongodb.close(); 29 | return callback(err) 30 | } 31 | collection.ensureIndex('user'); 32 | collection.insert(post, {safe: true}, function (err, post) { 33 | mongodb.close() 34 | callback(err, post); 35 | }); 36 | }); 37 | }); 38 | }; 39 | 40 | Post.get = function get(username, cb) { 41 | mongodb.open(function(err, db) { 42 | if (err) { 43 | return cb(err) 44 | }; 45 | 46 | db.collection('posts', function(err, collection) { 47 | if (err) { 48 | mongodb.close(); 49 | return cb(err); 50 | }; 51 | 52 | var query = {} 53 | if (username) { 54 | query.user = username; 55 | }; 56 | 57 | collection.find(query).sort({time:-1}).toArray(function(err, docs) { 58 | mongodb.close(); 59 | if (err) { 60 | cb(err,null) 61 | }; 62 | var posts = [] 63 | docs.forEach(function(doc, index) { 64 | var post = new Post(doc.user, doc.post, doc.time); 65 | posts.push(post) 66 | }); 67 | cb(null, posts); 68 | }); 69 | }); 70 | }); 71 | }; 72 | -------------------------------------------------------------------------------- /weibo/models/user.js: -------------------------------------------------------------------------------- 1 | 2 | /* 3 | * GET users listing. 4 | */ 5 | 6 | var mongodb = require('../models/db') 7 | 8 | function User(user) { 9 | this.name = user.name; 10 | this.password = user.password; 11 | }; 12 | module.exports = User; 13 | 14 | User.prototype.save = function save(cb) { 15 | var user = { 16 | name: this.name, 17 | password: this.password.toString(), 18 | }; 19 | mongodb.open(function(err, db) { 20 | if (err) { 21 | return cb(err); 22 | } 23 | 24 | db.collection('users',function(err, collection){ 25 | if (err) { 26 | mongodb.close(); 27 | return cb(err); 28 | } 29 | collection.ensureIndex('name', {unique:true}); 30 | 31 | collection.insert(user, {safe:true}, function(err, user) { 32 | mongodb.close(); 33 | cb(err, user); 34 | }); 35 | }); 36 | }); 37 | }; 38 | 39 | User.get = function get(username, cb) { 40 | //console.log(mongodb); 41 | mongodb.open(function (err, db) { 42 | if (err) { 43 | return cb(err); 44 | } 45 | 46 | //console.log(username); 47 | 48 | db.collection('users', function(err, collection) { 49 | if (err) { 50 | mongodb.close(); 51 | return cb(err); 52 | }; 53 | 54 | collection.findOne({name:username}, function(err, doc) { 55 | mongodb.close(); 56 | if (doc) { 57 | //console.log("lhj") 58 | //console.dir(doc) 59 | var user = new User(doc); 60 | //console.log('find the user'); 61 | //console.log(user); 62 | cb(err, user); 63 | } else { 64 | cb(err, null); 65 | } 66 | }); 67 | }); 68 | }); 69 | }; 70 | 71 | exports.list = function(req, res){ 72 | res.send("respond with a resource"); 73 | }; 74 | -------------------------------------------------------------------------------- /weibo/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "application-name", 3 | "version": "0.0.1", 4 | "private": true, 5 | "scripts": { 6 | "start": "node app.js" 7 | }, 8 | "dependencies": { 9 | "express": "3.4.3", 10 | "jade": "*", 11 | "connect-mongodb": ">= 1.0.0", 12 | "mongodb": ">= 0.9.9", 13 | "connect": ">= 2.0.0" 14 | } 15 | } 16 | -------------------------------------------------------------------------------- /weibo/public/img/glyphicons-halflings-white.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/liuhongjiang/blog_code/5a8c9a0c62669a17580a1a5fe133dca7bb94de06/weibo/public/img/glyphicons-halflings-white.png -------------------------------------------------------------------------------- /weibo/public/img/glyphicons-halflings.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/liuhongjiang/blog_code/5a8c9a0c62669a17580a1a5fe133dca7bb94de06/weibo/public/img/glyphicons-halflings.png -------------------------------------------------------------------------------- /weibo/public/stylesheets/style.css: -------------------------------------------------------------------------------- 1 | body { 2 | padding: 50px; 3 | font: 14px "Lucida Grande", Helvetica, Arial, sans-serif; 4 | } 5 | 6 | a { 7 | color: #00B7FF; 8 | } -------------------------------------------------------------------------------- /weibo/routes/index.js: -------------------------------------------------------------------------------- 1 | 2 | /* 3 | * GET home page. 4 | */ 5 | 6 | var crypto = require('crypto') 7 | 8 | var User = require('../models/user'); 9 | var Post = require('../models/post') 10 | 11 | //exports.index = function(req, res){ 12 | // res.render('index', { title: 'Express' }); 13 | //}; 14 | // 15 | //exports.user = function(req, res){ 16 | //}; 17 | // 18 | //exports.post = function(req, res){ 19 | //}; 20 | // 21 | //exports.reg = function(req, res){ 22 | //}; 23 | // 24 | //exports.doReg = function(req, res){ 25 | //}; 26 | // 27 | //exports.login = function(req, res){ 28 | //}; 29 | // 30 | //exports.doLogin = function(req, res){ 31 | //}; 32 | // 33 | //exports.logout = function(req, res){ 34 | //}; 35 | 36 | module.exports = function(app) { 37 | app.get('/', function(req, res) { 38 | Post.get(null, function(err, posts) { 39 | if (err) { 40 | posts = [] 41 | } 42 | res.render('index', { 43 | title: '首页', 44 | posts: posts, 45 | }); 46 | }); 47 | }); 48 | 49 | app.get('/reg', checkNotLogin) 50 | app.get('/reg', function(req, res){ 51 | res.render('reg', { 52 | title:'用户注册' 53 | }); 54 | }); 55 | 56 | app.post('/reg', checkNotLogin); 57 | app.post('/reg', function(req, res) { 58 | req.session.error = null; 59 | req.session.success = null; 60 | if (req.body['password-repeat'] != req.body['password']) { 61 | req.session.error = '两次输入的口令不一致'; 62 | return res.redirect('/reg'); 63 | } 64 | 65 | var md5 = crypto.createHash('md5'); 66 | var password = md5.update(req.body.password).digest('base64'); 67 | 68 | var newUser = new User({ 69 | name: req.body.username, 70 | password: password, 71 | }); 72 | 73 | User.get(newUser.name, function(err, user) { 74 | if (user) 75 | err = 'Username already exists 用户已经存在!'; 76 | if (err) { 77 | req.session.error = err; 78 | return res.redirect('/reg'); 79 | } 80 | 81 | newUser.save(function(err) { 82 | if (err) { 83 | req.session.error = err; 84 | return res.redirect('/reg'); 85 | } 86 | req.session.user = newUser; 87 | req.session.success = '注册成功'; 88 | res.redirect('/'); 89 | }); 90 | }); 91 | }); 92 | 93 | app.get('/login', function(req, res) { 94 | req.session.error = null; 95 | req.session.success = null; 96 | res.render('login',{ 97 | title:'用户登入', 98 | }); 99 | }); 100 | 101 | app.post('/login', checkNotLogin); 102 | app.post('/login', function(req, res) { 103 | req.session.error = null; 104 | req.session.success = null; 105 | var md5 = crypto.createHash('md5'); 106 | var password = md5.update(req.body.password).digest('base64'); 107 | //console.log(req.body.password); 108 | 109 | //console.log(req.body.username); 110 | 111 | User.get(req.body.username, function(err, user) { 112 | req.session.error = null; 113 | req.session.success = null; 114 | if (!user) { 115 | req.session.error = '用户不存在'; 116 | return res.redirect('/login'); 117 | } 118 | //console.log('-------------------------') 119 | //console.log(user.password.toString()) 120 | //console.log(user.password) 121 | //console.log('-------------------------') 122 | //console.log(password.toString()); 123 | //console.log(password); 124 | if (user.password != password) { 125 | req.session.error = '用户口令错误'; 126 | return res.redirect('/login'); 127 | } 128 | 129 | req.session.user = user; 130 | req.session.success = '登入成功'; 131 | res.redirect('/'); 132 | }); 133 | }); 134 | 135 | app.get('/logout', checkLogin) 136 | app.get('/logout', function(req,res){ 137 | req.session.user = null; 138 | req.session.error = null; 139 | req.session.success = '登出成功'; 140 | res.redirect('/'); 141 | }) 142 | 143 | app.post('/post', checkLogin); 144 | app.post('/post', function(req, res) { 145 | var currentUser = req.session.user; 146 | var post = new Post(currentUser.name, req.body.post); 147 | //console.log(post) 148 | post.save(function(err) { 149 | if (err) { 150 | req.session.error = err; 151 | return res.redirect('/'); 152 | } 153 | req.session.success = 'post successfully!'; 154 | res.redirect('/u/' + currentUser.name); 155 | }); 156 | }); 157 | 158 | app.get('/u/:user', function(req, res) { 159 | //console.log('req.session.user = %s', req.session.user); 160 | User.get(req.params.user, function(err, user) { 161 | if (!user) { 162 | req.session.error = "user not exist"; 163 | return res.redirect('/'); 164 | } 165 | Post.get(user.name, function(err, posts) { 166 | if (err) { 167 | req.session.error = err; 168 | return res.redirect('/') 169 | }; 170 | res.render('user', { 171 | title: user.name, 172 | posts: posts, 173 | }) 174 | }); 175 | }); 176 | }); 177 | }; 178 | 179 | function checkLogin(req, res, next) { 180 | req.session.error = null; 181 | req.session.success = null; 182 | if(!req.session.user) { 183 | req.session.error = 'Not login'; 184 | return res.redirect('login') 185 | } 186 | next(); 187 | } 188 | 189 | function checkNotLogin(req, res, next) { 190 | req.session.error = null; 191 | req.session.success = null; 192 | if (req.session.user) { 193 | req.session.error = 'Already login'; 194 | return res.redirect('/') 195 | } 196 | next() 197 | } 198 | -------------------------------------------------------------------------------- /weibo/settings.js: -------------------------------------------------------------------------------- 1 | module.exports = { 2 | cookiesSecret:'microblogbyvoid', 3 | db:'microblog', 4 | host:'localhost', 5 | }; 6 | -------------------------------------------------------------------------------- /weibo/test/testmongo.js: -------------------------------------------------------------------------------- 1 | var settings = require('../settings') 2 | var Db = require('mongodb').Db 3 | var Connection = require('mongodb').Connection 4 | var Server = require('mongodb').Server 5 | console.log(settings.host) 6 | console.log(Connection.DEFAULT_PORT) 7 | 8 | var mongodb = new Db(settings.db, new Server(settings.host, Connection.DEFAULT_PORT, {})); 9 | 10 | mongodb.open(function (err, db) { 11 | if (err) { 12 | console.log(err); 13 | } 14 | 15 | db.collection('users', function(err, collection) { 16 | if (err) { 17 | mongodb.close(); 18 | return console.log(err); 19 | }; 20 | 21 | collection.findOne({name:'aaa'}, function(err, doc) { 22 | mongodb.close(); 23 | if (doc) { 24 | console.log(doc); 25 | } else { 26 | console.log(err); 27 | } 28 | }); 29 | }); 30 | }); -------------------------------------------------------------------------------- /weibo/views/index.jade: -------------------------------------------------------------------------------- 1 | extends layout 2 | 3 | block content 4 | if success 5 | div.alert.alert-success= success 6 | if error 7 | div.alert.alert-error= error 8 | if !user 9 | div.hero-unit 10 | h1 欢迎来到Microblog 11 | p Microblog 是一个基于 Node.js的微博系统 12 | p 13 | a.btn.btn-primary.btn-large(href='/login') 登录 14 | a.btn.btn-large(href='/reg') 立即注册 15 | else 16 | include say 17 | include posts 18 | -------------------------------------------------------------------------------- /weibo/views/index_old.jade: -------------------------------------------------------------------------------- 1 | extends layout 2 | 3 | block content 4 | if success 5 | div.alert.alert-success= success 6 | if error 7 | div.alert.alert-error= error 8 | div.hero-unit 9 | h1 欢迎来到Microblog 10 | p Microblog 是一个基于 Node.js的微博系统 11 | p 12 | a.btn.btn-primary.btn-large(href='/login') 登录 13 | a.btn.btn-large(href='/reg') 立即注册 14 | 15 | div.row 16 | div.span4 17 | h2 Carbo 说 18 | p 东风破早梅 向暖一枝开 冰雪无人见 春从天上来 19 | 20 | div.span4 21 | h2 BYVOID 说 22 | p Open Chinese Convert（OpenCC ）是一个开源的中文简繁转换项目， 23 | | 致力于制作高质量的基于统计预料的简繁转换词库。 24 | | 还提供函数库(libopencc) 、命令行简繁转换工具、人工校对工具、词典生成程序、 25 | | 在线转换服务及图形用户界面。 26 | div.span4 27 | h2 佛振 说 28 | p 中州韵输入法引擎 / Rime Input Method Engine 取意历史上通行的中州韵， 29 | | 愿写就一部汇集音韵学智慧的输入法经典之作。 30 | | 项目网站设在 http://code.google.com/p/rimeime/ 31 | | 创造应用价值是一方面，更要坚持对好技术的追求，希望能写出灵动而易于扩展的代码， 32 | | 使其成为一款个性十足的开源输入法。 33 | -------------------------------------------------------------------------------- /weibo/views/layout.jade: -------------------------------------------------------------------------------- 1 | doctype 5 2 | html 3 | head 4 | title 5 | = title 6 | | Miroblog 7 | link(rel='stylesheet', href='/stylesheets/bootstrap.css') 8 | style(type='text/css'). 9 | body { 10 | padding-top: 60px; 11 | padding-bottom: 40px; 12 | } 13 | link(href='/stylesheets/bootstrap-responsive.css' rel='stylesheet') 14 | body 15 | div.navbar.navbar-fixed-top 16 | div.navbar-inner 17 | div.container 18 | a.btn.btn-navbar(data-toggle='collapse' data-target='.nav-collapse') 19 | span.icon-bar 20 | span.icon-bar 21 | span.icon-bar 22 | a.brand(href='/') Miroblog 23 | div.nav-collapse 24 | ul.nav 25 | li.active 26 | a(href='/') 首页 27 | if !user 28 | li 29 | a(href='/login') 登入 30 | li 31 | a(href='/reg') 注册 32 | else 33 | li 34 | a(href='/logout') 登出 35 | div#container.container 36 | block content 37 | hr 38 | footer 39 | p 40 | a(href='http://www.byvoid.com/' target='_blank') BYVOID 41 | | 2012 42 | script(src='/javascripts/jquery-1.10.2.min.js') 43 | script(src='/javascripts/bootstrap.js') 44 | -------------------------------------------------------------------------------- /weibo/views/login.jade: -------------------------------------------------------------------------------- 1 | extends layout 2 | 3 | block content 4 | if success 5 | div.alert.alert-success= success 6 | if error 7 | div.alert.alert-error= error 8 | form.form-horizontal(method='post') 9 | fieldset 10 | legend 用户登入 11 | div.control-group 12 | label.control-label(for='username') 用户名 13 | div.controls 14 | input.input-xlarge#username(type='text' name='username') 15 | div.control-group 16 | label.control-label(for=password) 口令 17 | div.controls 18 | input.input-xlarge#password(type='password' name='password') 19 | div.form-actions 20 | button.btn.btn-primary(type='submit') 登入 21 | 22 | -------------------------------------------------------------------------------- /weibo/views/posts.jade: -------------------------------------------------------------------------------- 1 | mixin oneblock(myposts) 2 | div.row 3 | - each post in myposts 4 | div.span4 5 | h2 6 | a(href='/u/#{post.user}')= post.user 7 | | 说 8 | p 9 | small= post.time 10 | p= post.post 11 | 12 | - var splitPosts = [] 13 | 14 | 15 | each post, idx in posts 16 | if idx % 3 == 0 17 | if splitPosts.length != 0 18 | +oneblock(splitPosts) 19 | - splitPosts = [] 20 | - splitPosts.push(post) 21 | 22 | 23 | if splitPosts.length != 0 24 | +oneblock(splitPosts) 25 | - splitPosts = [] 26 | -------------------------------------------------------------------------------- /weibo/views/reg.jade: -------------------------------------------------------------------------------- 1 | extends layout 2 | 3 | block content 4 | if success 5 | div.alert.alert-success= success 6 | if error 7 | div.alert.alert-error= error 8 | 9 | form.form-horizontal(method='post') 10 | fieldset 11 | legend 用户注册 12 | div.control-group 13 | label.control-label(for='username') 用户名 14 | div.controls 15 | input.input-xlarge#username(type='text' name='username') 16 | p.help-block 你的帐号名称，用于登录和显示 17 | 18 | div.control-group 19 | label.control-label(for='password') 口令 20 | div.controls 21 | input.input-xlarge#password(type='password' name='password') 22 | 23 | div.control-group 24 | label.control-label(for='password-repeat') 重复输入口令 25 | div.controls 26 | input.input-xlarge#password-repeat(type='password' name='password-repeat') 27 | 28 | div.form-actions 29 | button.btn.btn-primary(type='submit') 注册 30 | 31 | -------------------------------------------------------------------------------- /weibo/views/say.jade: -------------------------------------------------------------------------------- 1 | form.well.form-inline.center(method='post' action='/post' style='text-aglign:center;') 2 | input.span8(type='text' name='post') 3 | button.btn.btn-success(type='submit') 4 | i.icon-comment.icon-white 5 | | 发言 6 | 7 | -------------------------------------------------------------------------------- /weibo/views/user.jade: -------------------------------------------------------------------------------- 1 | extends layout 2 | 3 | block content 4 | if user 5 | include say 6 | include posts 7 | --------------------------------------------------------------------------------