1
00:00:09,000 --> 00:00:10,000


2
00:00:10,000 --> 00:00:13,000
Esta apresentação é disponibilizada pelo centro Standford para desenvolvimento

3
00:00:13,000 --> 00:00:20,000
profissional

4
00:00:23,000 --> 00:00:26,000
Ok. Bom dia e bem vindos de volta

5
00:00:26,000 --> 00:00:30,000
a terceira palestra para esta classe. Então,

6
00:00:30,000 --> 00:00:33,000
aqui está o que quero fazer hoje

7
00:00:33,000 --> 00:00:34,000
E... em alguns dos

8
00:00:34,000 --> 00:00:37,000
tópicos que eu irei fazer hoje podem parecer um pouco que eu esteja meio que, pulando

9
00:00:37,000 --> 00:00:41,000
de um tópico a outro, mas aqui está, mais ou menos, o roteiro para hoje e o

10
00:00:41,000 --> 00:00:44,000
fluxo lógico de idéias.

11
00:00:44,000 --> 00:00:48,000
Na última palestra falamos sobre regressão linear e hoje eu quero falar sobre

12
00:00:48,000 --> 00:00:52,000
um tipo de adaptação dela chamada (locally weighted regression). É um algoritmo

13
00:00:52,000 --> 00:00:57,000
muito popular, na verdade este provavelmente é um dos algoritmos de aprendizado de máquina

14
00:00:57,000 --> 00:00:59,000
favoritos de um de meus antigos mentores. Então

15
00:00:59,000 --> 00:01:02,000
iremos falar sobre uma provável segunda interpretação da regressão linear

16
00:01:02,000 --> 00:01:07,000
E usá-la para nos mover ao nosso primeiro algoritmo de classificação

17
00:01:07,000 --> 00:01:08,000
que é regressão logística

18
00:01:08,000 --> 00:01:12,000
Tomaremos uma pequena digressão para falar sobre uma coisa chamada algoritmo 'perceptron'

19
00:01:12,000 --> 00:01:15,000
que é algo que nós iremos voltar a discutir, novamente, mais tarde neste trimestre

20
00:01:15,000 --> 00:01:17,000
e

21
00:01:17,000 --> 00:01:21,000
Se o tempo permitir, eu espero chegar ao método de Newton, o qual é um algoritmo para

22
00:01:21,000 --> 00:01:23,000
ajustar modelos de

23
00:01:23,000 --> 00:01:24,000
regressão logística.

24
00:01:24,000 --> 00:01:30,000
Então, recapitulando onde nós paramos na palestra anterior,

25
00:01:30,000 --> 00:01:33,000
lembrem-se que a notação que eu defini foi que

26
00:01:33,000 --> 00:01:35,000
eu usei isto

27
00:01:35,000 --> 00:01:37,000
X, i sobrescrito

28
00:01:37,000 --> 00:01:44,000
Y, i sobrescrito, para denotaro exemplo de treinamento i

29
00:01:47,000 --> 00:01:49,000
e

30
00:01:49,000 --> 00:01:51,000
quando estávamos falando sobre regressão linear

31
00:01:51,000 --> 00:01:52,000


32
00:01:52,000 --> 00:01:54,000
ou método dos mínimos quadrados

33
00:01:54,000 --> 00:01:56,000
Nós usamos isto para denotar

34
00:01:56,000 --> 00:01:58,000
o valor predito resultado

35
00:01:58,000 --> 00:02:00,000
da aplicação de minha "hipótese" H

36
00:02:00,000 --> 00:02:02,000
na entrada Xi

37
00:02:02,000 --> 00:02:04,000
E minha hipótese

38
00:02:04,000 --> 00:02:06,000
era parametrizada pelo

39
00:02:06,000 --> 00:02:08,000
vetor de parâmetros theta

40
00:02:08,000 --> 00:02:13,000
E então dizemos que isto era igual à soma de j = 0 á n

41
00:02:13,000 --> 00:02:15,000
da multiplicação de theta j por xj

42
00:02:15,000 --> 00:02:19,000
i superscrito

43
00:02:19,000 --> 00:02:22,000
que é produto de theta transposto por X

44
00:02:22,000 --> 00:02:25,000
E temos a convenção que X

45
00:02:25,000 --> 00:02:29,000
zero subscrito, é igual a 1, Então isto conta para a intersecção no nosso

46
00:02:29,000 --> 00:02:31,000
modelo de regressão linear

47
00:02:31,000 --> 00:02:33,000
E o n minúsculo aqui

48
00:02:33,000 --> 00:02:36,000
era a notação que eu estava usando para

49
00:02:36,000 --> 00:02:40,000
o número de 'features' no meu conjunto de Treinamento. Ok? Então

50
00:02:40,000 --> 00:02:43,000
no exemplo quando tentamos predizer o preço de casas, nós tínhamos duas 'features, o tamanho

51
00:02:43,000 --> 00:02:45,000
da casa e o número de quartos

52
00:02:45,000 --> 00:02:50,000
Nós tínhamos duas features, portanto, n-zinho era igual a 2

53
00:02:50,000 --> 00:02:51,000


54
00:02:51,000 --> 00:02:54,000
Então, apenas para

55
00:02:54,000 --> 00:02:57,000
terminar de recapitular a palestra anterior

56
00:02:57,000 --> 00:03:01,000
nós definimos esta função de custo quadrática J de theta

57
00:03:01,000 --> 00:03:05,000
era igual a 1/2, soma de i = 1 à m ....

58
00:03:05,000 --> 00:03:07,000
...

59
00:03:07,000 --> 00:03:10,000
...

60
00:03:10,000 --> 00:03:12,000
ao quadrdado

61
00:03:12,000 --> 00:03:16,000
onde esta é a soma sobre nossos m exemplos de treinamento e meu conjunto de treinamento. Então

62
00:03:16,000 --> 00:03:17,000
m minúsculo

63
00:03:17,000 --> 00:03:21,000
era a notação que eu estava usando para denotar o número de exemplos de treinamento que eu tinha

64
00:03:21,000 --> 00:03:23,000
e o tamanho do meu conjunto de treinamento

65
00:03:23,000 --> 00:03:25,000
E no fim da última palestra

66
00:03:25,000 --> 00:03:26,000
nós derivamos

67
00:03:26,000 --> 00:03:30,000
o valor de theta que minimiza isto na forma fechada, que era X

68
00:03:30,000 --> 00:03:32,000
transposto X

69
00:03:32,000 --> 00:03:35,000
inverso X transposto

70
00:03:35,000 --> 00:03:38,000
Y. Ok?

71
00:03:38,000 --> 00:03:42,000
Então

72
00:03:42,000 --> 00:03:46,000
A medida que continuarmos a palestra de hoje, Eu continuarei a usar esta anotação e, novamente,

73
00:03:46,000 --> 00:03:50,000
Eu percebo que é uma grande quantidade de notações para todos lembrarem,

74
00:03:50,000 --> 00:03:55,000
Então se durante esta palestra você se esquecer - se você tiver problemas para se lembrar

75
00:03:55,000 --> 00:04:02,000
o que m minúsculo é ou o que n minúsculo é ou qualquer coisa, por favor levante sua mão e pergunte.

76
00:04:04,000 --> 00:04:07,000
Quando nós falamos sobre regressão linear da última vez

77
00:04:07,000 --> 00:04:10,000
nós usamos duas features. Uma delas era

78
00:04:10,000 --> 00:04:14,000
o tamanho das casas em 'pés' quadrados, Então a área de convivência da casa

79
00:04:14,000 --> 00:04:18,000
e a outra feature era o número de quartos na casa

80
00:04:18,000 --> 00:04:22,000
Geralmente, nós aplicamos um algoritmo de aprendizado de máquina a um problema que

81
00:04:22,000 --> 00:04:23,000
importa para você

82
00:04:23,000 --> 00:04:28,000
A escolha de features irá depender muito de você, certo?

83
00:04:28,000 --> 00:04:32,000
E o modo como você escolhe suas features para dar ao algoritmo, frequentemente irá

84
00:04:32,000 --> 00:04:34,000
causar um grande impacto na maneira como ele funciona.

85
00:04:34,000 --> 00:04:40,000
Então, por exemplo

86
00:04:40,000 --> 00:04:44,000
a escolha que nós fizemos da última vez era x1 igual ao tamanho.

87
00:04:44,000 --> 00:04:47,000
e vamos deixar esta idéia de feature do número de quartos por enquanto. Vamos dizer que nós não temos dados

88
00:04:47,000 --> 00:04:50,000
que nos digam quantos quartos há nestas casas

89
00:04:50,000 --> 00:04:54,000
Uma coisa que você poderia fazer é definir - oh, vamos

90
00:04:54,000 --> 00:04:56,000
desenhar isto

91
00:04:56,000 --> 00:05:03,000
E então...

92
00:05:04,000 --> 00:05:07,000
Então digamos que isto era o tamanho da casa e que este é o preço da casa, então

93
00:05:07,000 --> 00:05:10,000
Se você usar

94
00:05:10,000 --> 00:05:14,000
isto como uma feature, talvez você tenha theta0 mais theta1

95
00:05:14,000 --> 00:05:19,000
x1, é um tipo de, modelo linear.

96
00:05:19,000 --> 00:05:21,000
Se você escolher - deixe-me apenas copiar

97
00:05:21,000 --> 00:05:26,000
os mesmos dados de novo, certo?

98
00:05:26,000 --> 00:05:30,000
Você pode definir o conjunto de features onde x1 é igual ao tamanho da casa

99
00:05:30,000 --> 00:05:34,000
e x2 é

100
00:05:34,000 --> 00:05:36,000
o quadrado

101
00:05:36,000 --> 00:05:37,000
do tamanho

102
00:05:37,000 --> 00:05:38,000
da casa, ok?

103
00:05:38,000 --> 00:05:43,000
Então x1 é o tamanho da casa em, digamos, uma medida quadrada (e.g. m²) e x2 é

104
00:05:43,000 --> 00:05:45,000
qualquer medida quadrada da casa e apenas

105
00:05:45,000 --> 00:05:49,000
eleve esse número ao quadrado, e isto seria outro modo de escolher uma feature

106
00:05:49,000 --> 00:05:51,000
e se você fizer isto então

107
00:05:51,000 --> 00:05:55,000
o mesmo algoritmo irá se ajustar

108
00:05:55,000 --> 00:05:59,000
a uma função quadrática para você

109
00:05:59,000 --> 00:06:01,000
theta2, x1 ao quadrado

110
00:06:01,000 --> 00:06:06,000
ok? Porque isto

111
00:06:06,000 --> 00:06:09,000
na verdade é x2. E

112
00:06:09,000 --> 00:06:12,000
dependendo da aparência dos dados, talvez isto se ajuste

113
00:06:12,000 --> 00:06:16,000
levemente melhor aos dados. Na verdade você pode levar isto

114
00:06:16,000 --> 00:06:23,000
ainda mais adiante

115
00:06:25,000 --> 00:06:26,000
O que é - vamos ver

116
00:06:26,000 --> 00:06:30,000
Eu tenho sete exemplos de treinamento aqui, então na verdade você pode

117
00:06:30,000 --> 00:06:34,000
,talvez, ajustar até um polinômio de grau 6. Você poderia ajustar um modelo

118
00:06:34,000 --> 00:06:35,000
theta0 mais

119
00:06:35,000 --> 00:06:38,000
theta1, x1 mais theta2

120
00:06:38,000 --> 00:06:42,000
x ao quadrado mais etc...

121
00:06:42,000 --> 00:06:48,000
até theta6. X à

122
00:06:48,000 --> 00:06:52,000
potência de 6 e theta é o polinômio

123
00:06:52,000 --> 00:06:55,000
para estes sete pontos de dados

124
00:06:55,000 --> 00:06:58,000
E se você fizer isto, você vai descobrir que

125
00:06:58,000 --> 00:07:01,000
você criou um modelo que se ajusta exatamente aos seus dados. É aí que, eu acho,

126
00:07:01,000 --> 00:07:06,000
neste exemplo que eu desenhei, nós temos 7 pontos de dados, então se você ajusta um

127
00:07:06,000 --> 00:07:08,000
modelo de polinômio de grau 6, você pode, meio que, ajustar

128
00:07:08,000 --> 00:07:11,000
uma linha que passa por estes sete pontos perfeitamente

129
00:07:11,000 --> 00:07:14,000
e você provavelmente encontra uma curva que

130
00:07:14,000 --> 00:07:17,000
você iria conseguir algo

131
00:07:17,000 --> 00:07:20,000
parecido com isto

132
00:07:20,000 --> 00:07:23,000
E por um lado, é um ótimo modelo no sentido que ele

133
00:07:23,000 --> 00:07:25,000
se ajusta perfeitamente aos dados de treinamento

134
00:07:25,000 --> 00:07:26,000
Mas por outro, este provavelmente não é

135
00:07:26,000 --> 00:07:28,000
um bom modelo no sentido que

136
00:07:28,000 --> 00:07:31,000
nenhum de nós, realmente pensa que esta seja uma boa predição dos preços

137
00:07:31,000 --> 00:07:36,000
das casas, como uma função do tamanho da casa, certo? Então

138
00:07:36,000 --> 00:07:39,000
Na verdade, nós voltaremos a isto mais tarde. Acontece que

139
00:07:39,000 --> 00:07:41,000
nos modelos que nós temos aqui;

140
00:07:41,000 --> 00:07:45,000
Eu sinto que talvez o modelo quadrático se ajuste melhor

141
00:07:45,000 --> 00:07:46,000
Enquanto que

142
00:07:46,000 --> 00:07:47,000


143
00:07:47,000 --> 00:07:52,000
O modelo linear parece que possui um pouco de componente quadrático nestes

144
00:07:52,000 --> 00:07:52,000
dados

145
00:07:52,000 --> 00:07:56,000
que a função linear não está capturando

146
00:07:56,000 --> 00:07:59,000
Então nós iremos voltar a isto um pouco mais tarde e falar sobre os problemas

147
00:07:59,000 --> 00:08:03,000
associados com ajuste de modelos que são, ou muito simples, usam dos pequenos

148
00:08:03,000 --> 00:08:04,000
conjuntos de features, ou

149
00:08:04,000 --> 00:08:08,000
em modelos que são muito complexos e talvez

150
00:08:08,000 --> 00:08:11,000
usem um conjunto muito grande de featurees

151
00:08:11,000 --> 00:08:12,000
apenas para dar-lhes

152
00:08:12,000 --> 00:08:13,000
um nome,

153
00:08:13,000 --> 00:08:14,000
nós chamamos isto de

154
00:08:14,000 --> 00:08:19,000
o problema de 'underfitting' (subajuste)

155
00:08:19,000 --> 00:08:23,000
e, muito informalmente, isto se refere a uma configuração onde

156
00:08:23,000 --> 00:08:26,000
existem padrões óbvios que - onde há padrões nos dados que

157
00:08:26,000 --> 00:08:28,000
o algoritmo está falhando a se ajustar

158
00:08:28,000 --> 00:08:32,000
E a este problema aqui nós nos referimos como sendo

159
00:08:32,000 --> 00:08:34,000
overfitting (sobreajuste)

160
00:08:34,000 --> 00:08:36,000
e, novamente, muito informalmente,

161
00:08:36,000 --> 00:08:41,000
isto acontece quando o algoritmo está ajustando às Idiossincrasias de um conjunto de dados específicos

162
00:08:41,000 --> 00:08:43,000
certo? Isto acontece apenas porque

163
00:08:43,000 --> 00:08:48,000
das sete casas que tiramos amostras em Portland, ou de onde quer que você esteja coletando dados,

164
00:08:48,000 --> 00:08:51,000
esta casa acaba por ser um pouco mais cara, esta outro um pouco menos

165
00:08:51,000 --> 00:08:54,000
cara, e por

166
00:08:54,000 --> 00:08:57,000
ajustar seis ao polinômio nós, meio que, ajustamos características individuais

167
00:08:57,000 --> 00:08:58,000
deste conjunto de dados,

168
00:08:58,000 --> 00:09:01,000
Ao invés das verdadeiras tendências subjacentes

169
00:09:01,000 --> 00:09:04,000
de como o preço das casas varia como uma função do tamanho da casa. Ok?

170
00:09:04,000 --> 00:09:08,000
Então, estes são dois problemas muito diferentes. Nós iremos definí-los mais formalmente mais tarde.

171
00:09:08,000 --> 00:09:12,000
e falar sobre como atacar cada um destes problemas.

172
00:09:12,000 --> 00:09:13,000
Mas por agora eu

173
00:09:13,000 --> 00:09:20,000
espero apreciar que há este assunto de seleção de features.

174
00:09:22,000 --> 00:09:23,000
Então se você quiser apenas

175
00:09:23,000 --> 00:09:26,000
ensinar-nos os problemas de aprendizado, existem poucas maneiras de fazê-lo

176
00:09:26,000 --> 00:09:27,000
Então

177
00:09:27,000 --> 00:09:29,000
Nós iremos falar sobre

178
00:09:29,000 --> 00:09:32,000
algoritmos de seleção de features, mais tarde neste trimestre também. Então algoritmos automáticos

179
00:09:32,000 --> 00:09:33,000
para escolher

180
00:09:33,000 --> 00:09:35,000
que features usar em um

181
00:09:35,000 --> 00:09:37,000
problema de regressão como este

182
00:09:37,000 --> 00:09:41,000
O que eu quero fazer hoje é falar sobre uma classe de algoritmos

183
00:09:41,000 --> 00:09:44,000
chamados de algoritmos de aprendizado não paramétricos que irão ajudar

184
00:09:44,000 --> 00:09:49,000
de alguma forma, a aliviar a necessidade de você escolher features muito cuidadosamente. Ok?

185
00:09:49,000 --> 00:09:56,000
E isto nos leva a nossa discussão sobre (locally weighted regression).

186
00:09:56,000 --> 00:10:03,000
E só pra definir o termo

187
00:10:12,000 --> 00:10:16,000
A regressão linear, da medida que definimos até agora, é um exemplo de um algoritmo de aprendizado paramétrico

188
00:10:16,000 --> 00:10:17,000
e

189
00:10:17,000 --> 00:10:19,000
algoritmo de aprendizado paramétrico

190
00:10:19,000 --> 00:10:21,000
da maneira como é definido é

191
00:10:21,000 --> 00:10:24,000
um algoritmo que tem um número fixo de parâmetros

192
00:10:24,000 --> 00:10:27,000
Que se ajustam aos dados, Então

193
00:10:27,000 --> 00:10:28,000
Na regressão linear

194
00:10:28,000 --> 00:10:32,000
nós temos um conjunto fixo de parâmetros theta. Que deve

195
00:10:32,000 --> 00:10:39,000
se ajustar aos dados

196
00:10:39,000 --> 00:10:46,000
Em contraste, o que eu vou falar sobre agora é nosso primeiro algoritmo de aprendizado não-paramétrico.

197
00:10:58,000 --> 00:11:02,000
A definição formal, a qual não é muito intuitiva, eu substitui por uma

198
00:11:02,000 --> 00:11:04,000
segunda, digamos, mais

199
00:11:04,000 --> 00:11:06,000
intuitiva.

200
00:11:06,000 --> 00:11:10,000
O tipo de definição formal do algoritmo de aprendizado não paramétrico é de um algoritmo

201
00:11:10,000 --> 00:11:17,000
onde o número de paramâmetros

202
00:11:18,000 --> 00:11:22,000
cresce

203
00:11:22,000 --> 00:11:25,000
com M, com o tramanho do conjunto de treinamento. E geralmente é

204
00:11:25,000 --> 00:11:30,000
definido como um número de parâmetros cresce linearmente com o tamanho do conjunto de treinamento.

205
00:11:30,000 --> 00:11:32,000
Esta é a definição formal

206
00:11:32,000 --> 00:11:33,000
Uma

207
00:11:33,000 --> 00:11:36,000
definição levemente menos formal é que

208
00:11:36,000 --> 00:11:37,000
A quantidade de coisa que seu algoritmo de aprendizado precisa

209
00:11:37,000 --> 00:11:40,000
para se manter funcionando

210
00:11:40,000 --> 00:11:44,000
irá crescer linearmente com o conjunto de treinamento ou, outra forma de dizer isto, é um

211
00:11:44,000 --> 00:11:45,000
algoritmo que

212
00:11:45,000 --> 00:11:51,000
Nós iremos precisar manter o conjunto de treinamento inteiro, mesmo depois do aprendizado. Ok? Então

213
00:11:51,000 --> 00:11:53,000
Não se preocupe demais sobre esta definição. Mas

214
00:11:53,000 --> 00:11:55,000
O que eu quero agora é

215
00:11:55,000 --> 00:11:58,000
descrever um algoritmo de aprendizado não-paramétrico específico

216
00:11:58,000 --> 00:12:05,000
chamado "locally weighted regression"

217
00:12:09,000 --> 00:12:16,000
O qual também recebe um par de outros nomes

218
00:12:17,000 --> 00:12:20,000
O qual também é nomeado como Loess, por razões meio históricas. Loess

219
00:12:20,000 --> 00:12:23,000
Geralmente é soletrado L-O-E-S-S

220
00:12:23,000 --> 00:12:24,000
algumas vezes falando assim,

221
00:12:24,000 --> 00:12:27,000
também. Eu só chamo de "locally weighted regression"

222
00:12:27,000 --> 00:12:34,000
Então aqui está

223
00:12:34,000 --> 00:12:37,000
a idéia. Este será um algoritmo que vai nos permitir

224
00:12:37,000 --> 00:12:42,000
nos preocupar um pouco menos sobre ter que escolher features muito cuidadosamente.

225
00:12:42,000 --> 00:12:48,000
Então

226
00:12:48,000 --> 00:12:55,000
Para meu exemplo de motivação vamos dizer que eu

227
00:12:55,000 --> 00:12:59,000
tenho um

228
00:12:59,000 --> 00:13:00,000
conjunto de terinamento que parece com este, ok?

229
00:13:00,000 --> 00:13:04,000
Então isto é X e isto é Y

230
00:13:04,000 --> 00:13:07,000
Se você executar

231
00:13:07,000 --> 00:13:10,000
regressão linear nisto e você ajustar talvez, uma função linear a isto

232
00:13:10,000 --> 00:13:12,000
você acaba com uma

233
00:13:12,000 --> 00:13:13,000
mais ou menos

234
00:13:13,000 --> 00:13:16,000
linha reta, a qual não é um ajuste muito bom para esses dados.

235
00:13:16,000 --> 00:13:19,000
Você ainda pode sentar e encarar isto e tentar decidir que features são usadas certo

236
00:13:19,000 --> 00:13:22,000
Então talvez você queira jogar uma função quadrática

237
00:13:22,000 --> 00:13:25,000
Mas ela não é realmente quadrática também. Então talvez você queira

238
00:13:25,000 --> 00:13:27,000
modelar isto como um X

239
00:13:27,000 --> 00:13:31,000
mais X ao quadrado mas talvez alguma função do seno de X ou algo do tipo

240
00:13:31,000 --> 00:13:33,000
Na verdade você pode sentar e perder tempo com features

241
00:13:33,000 --> 00:13:37,000
E depois de um tempo você provavelmente virá com um conjunto de features cujo modelo está

242
00:13:37,000 --> 00:13:39,000
ok, mas vamos falar de um algoritmo que

243
00:13:39,000 --> 00:13:46,000
você pode usar sem necessidade de fazer isso

244
00:13:50,000 --> 00:13:52,000
Então

245
00:13:52,000 --> 00:13:54,000
Se - bem

246
00:13:54,000 --> 00:13:56,000
suponha que você quer avaliar

247
00:13:56,000 --> 00:13:59,000
sua hipótese H

248
00:13:59,000 --> 00:14:03,000
em um certo ponto.

249
00:14:03,000 --> 00:14:06,000
Com um certo ponto de requisição em X. ok? e

250
00:14:06,000 --> 00:14:07,000
vamos dizer que

251
00:14:07,000 --> 00:14:10,000
você quer saber qual o valor predito de

252
00:14:10,000 --> 00:14:11,000
Y

253
00:14:11,000 --> 00:14:16,000
nesta posição de X, certo? Então

254
00:14:16,000 --> 00:14:18,000
para a regressão linear

255
00:14:18,000 --> 00:14:22,000
O que nós estamos fazemos era ajustar

256
00:14:22,000 --> 00:14:25,000
theta

257
00:14:25,000 --> 00:14:28,000
para minimizar

258
00:14:28,000 --> 00:14:30,000
a soma sobre i

259
00:14:30,000 --> 00:14:34,000
Yi menos theta, transposto Xi

260
00:14:34,000 --> 00:14:38,000
ao quadrado e retornando theta

261
00:14:38,000 --> 00:14:41,000
transposto X. Ok?

262
00:14:41,000 --> 00:14:46,000
Então esta era regressão linear

263
00:14:46,000 --> 00:14:49,000
Em contraste, em "locally weighted linear regression" você irá fazer as coisas levemente

264
00:14:49,000 --> 00:14:51,000
diferente. ok?

265
00:14:51,000 --> 00:14:54,000
vamos olhar para este ponto X

266
00:14:54,000 --> 00:14:58,000
e então eu irei olhar no meu conjunto de dados e levar em consideração

267
00:14:58,000 --> 00:15:03,000
apenas o conjunto de pontos, meio que, em uma pequena vizinhança de X. Ok?

268
00:15:03,000 --> 00:15:07,000
Então nós iremos olhar onde Eu quero avaliar minha hipótese. Eu vou olhar

269
00:15:07,000 --> 00:15:10,000
apenas a vizinhança

270
00:15:10,000 --> 00:15:13,000
deste ponto. Onde eu quero avaliar minha hipótese

271
00:15:13,000 --> 00:15:16,000
E então eu vou pegar,

272
00:15:16,000 --> 00:15:19,000
digamos, apenas estes poucos pontos

273
00:15:19,000 --> 00:15:21,000
e Irei

274
00:15:21,000 --> 00:15:22,000
aplicar regressão linear

275
00:15:22,000 --> 00:15:26,000
para ajustar uma linha reta apenas para este subcojunto dos dados, ok? Eu

276
00:15:26,000 --> 00:15:29,000
irei usar este sub-termo subconjunto onde nós iremos voltar novamente

277
00:15:29,000 --> 00:15:32,000
Então nós temos este conjunto de dados e Eu ajustei uma linha reta

278
00:15:32,000 --> 00:15:36,000
a ele e talvez eu tenha uma linha como esta

279
00:15:36,000 --> 00:15:40,000
e O que eu irei fazer então é

280
00:15:40,000 --> 00:15:45,000
avaliar este valor particular na linha reta

281
00:15:45,000 --> 00:15:47,000
e este será o valor que eu retorno para meu algoritmo

282
00:15:47,000 --> 00:15:50,000
Eu acho que isto seria o valor predito

283
00:15:50,000 --> 00:15:53,000
para

284
00:15:53,000 --> 00:15:57,000
Isto seria o valor que minha hipótese retornaria

285
00:15:57,000 --> 00:16:04,000
em "locally weighted regression" ok? Então

286
00:16:05,000 --> 00:16:10,000
nós iremos seguir em frente. Deixe me continuar e formalizar isto

287
00:16:10,000 --> 00:16:15,000


288
00:16:15,000 --> 00:16:18,000


289
00:16:18,000 --> 00:16:25,000


290
00:16:27,000 --> 00:16:32,000


291
00:16:32,000 --> 00:16:36,000


292
00:16:36,000 --> 00:16:37,000


293
00:16:37,000 --> 00:16:41,000


294
00:16:41,000 --> 00:16:42,000


295
00:16:42,000 --> 00:16:45,000


296
00:16:45,000 --> 00:16:49,000


297
00:16:49,000 --> 00:16:54,000


298
00:16:54,000 --> 00:16:57,000


299
00:16:57,000 --> 00:17:04,000


300
00:17:06,000 --> 00:17:10,000


301
00:17:10,000 --> 00:17:14,000


302
00:17:14,000 --> 00:17:15,000


303
00:17:15,000 --> 00:17:19,000


304
00:17:19,000 --> 00:17:21,000


305
00:17:21,000 --> 00:17:25,000


306
00:17:25,000 --> 00:17:27,000


307
00:17:27,000 --> 00:17:30,000


308
00:17:30,000 --> 00:17:31,000


309
00:17:31,000 --> 00:17:34,000


310
00:17:34,000 --> 00:17:41,000


311
00:17:42,000 --> 00:17:43,000


312
00:17:43,000 --> 00:17:48,000


313
00:17:48,000 --> 00:17:51,000


314
00:17:51,000 --> 00:17:51,000


315
00:17:51,000 --> 00:17:56,000


316
00:17:56,000 --> 00:18:02,000


317
00:18:02,000 --> 00:18:05,000


318
00:18:05,000 --> 00:18:12,000


319
00:18:12,000 --> 00:18:13,000


320
00:18:13,000 --> 00:18:16,000


321
00:18:16,000 --> 00:18:21,000


322
00:18:21,000 --> 00:18:24,000


323
00:18:24,000 --> 00:18:28,000


324
00:18:28,000 --> 00:18:32,000


325
00:18:32,000 --> 00:18:35,000


326
00:18:35,000 --> 00:18:37,000


327
00:18:37,000 --> 00:18:39,000


328
00:18:39,000 --> 00:18:44,000


329
00:18:44,000 --> 00:18:47,000


330
00:18:47,000 --> 00:18:48,000


331
00:18:48,000 --> 00:18:54,000


332
00:18:54,000 --> 00:18:55,000


333
00:18:55,000 --> 00:18:58,000


334
00:18:58,000 --> 00:19:00,000


335
00:19:00,000 --> 00:19:02,000


336
00:19:02,000 --> 00:19:05,000


337
00:19:05,000 --> 00:19:06,000


338
00:19:06,000 --> 00:19:10,000


339
00:19:10,000 --> 00:19:17,000


340
00:19:17,000 --> 00:19:22,000


341
00:19:22,000 --> 00:19:26,000


342
00:19:26,000 --> 00:19:29,000


343
00:19:29,000 --> 00:19:33,000


344
00:19:33,000 --> 00:19:35,000


345
00:19:35,000 --> 00:19:37,000


346
00:19:37,000 --> 00:19:41,000


347
00:19:41,000 --> 00:19:42,000


348
00:19:42,000 --> 00:19:45,000


349
00:19:45,000 --> 00:19:47,000


350
00:19:47,000 --> 00:19:49,000


351
00:19:49,000 --> 00:19:51,000


352
00:19:51,000 --> 00:19:53,000


353
00:19:53,000 --> 00:19:55,000


354
00:19:55,000 --> 00:19:59,000


355
00:19:59,000 --> 00:20:02,000


356
00:20:02,000 --> 00:20:06,000


357
00:20:06,000 --> 00:20:07,000


358
00:20:07,000 --> 00:20:12,000


359
00:20:12,000 --> 00:20:13,000


360
00:20:13,000 --> 00:20:16,000


361
00:20:16,000 --> 00:20:21,000


362
00:20:21,000 --> 00:20:23,000


363
00:20:23,000 --> 00:20:28,000


364
00:20:28,000 --> 00:20:32,000


365
00:20:32,000 --> 00:20:33,000


366
00:20:33,000 --> 00:20:36,000


367
00:20:36,000 --> 00:20:39,000


368
00:20:39,000 --> 00:20:42,000


369
00:20:42,000 --> 00:20:44,000


370
00:20:44,000 --> 00:20:48,000


371
00:20:48,000 --> 00:20:50,000


372
00:20:50,000 --> 00:20:53,000


373
00:20:53,000 --> 00:20:54,000


374
00:20:54,000 --> 00:20:56,000


375
00:20:56,000 --> 00:20:58,000


376
00:20:58,000 --> 00:21:01,000


377
00:21:01,000 --> 00:21:04,000


378
00:21:04,000 --> 00:21:07,000


379
00:21:07,000 --> 00:21:10,000


380
00:21:10,000 --> 00:21:11,000


381
00:21:11,000 --> 00:21:15,000


382
00:21:15,000 --> 00:21:17,000


383
00:21:17,000 --> 00:21:21,000


384
00:21:21,000 --> 00:21:24,000


385
00:21:24,000 --> 00:21:27,000


386
00:21:27,000 --> 00:21:32,000


387
00:21:32,000 --> 00:21:33,000


388
00:21:33,000 --> 00:21:40,000


389
00:21:40,000 --> 00:21:47,000


390
00:21:47,000 --> 00:21:49,000


391
00:21:49,000 --> 00:21:53,000


392
00:21:53,000 --> 00:21:55,000


393
00:21:55,000 --> 00:21:57,000


394
00:21:57,000 --> 00:22:00,000


395
00:22:00,000 --> 00:22:04,000


396
00:22:04,000 --> 00:22:08,000


397
00:22:08,000 --> 00:22:09,000


398
00:22:09,000 --> 00:22:16,000


399
00:22:17,000 --> 00:22:21,000


400
00:22:21,000 --> 00:22:25,000


401
00:22:25,000 --> 00:22:27,000


402
00:22:27,000 --> 00:22:30,000


403
00:22:30,000 --> 00:22:32,000


404
00:22:32,000 --> 00:22:39,000


405
00:22:41,000 --> 00:22:44,000


406
00:22:44,000 --> 00:22:45,000


407
00:22:45,000 --> 00:22:46,000


408
00:22:46,000 --> 00:22:50,000


409
00:22:50,000 --> 00:22:51,000


410
00:22:51,000 --> 00:22:52,000


411
00:22:52,000 --> 00:22:55,000


412
00:22:55,000 --> 00:22:56,000


413
00:22:56,000 --> 00:22:58,000


414
00:22:58,000 --> 00:23:00,000


415
00:23:00,000 --> 00:23:04,000


416
00:23:04,000 --> 00:23:06,000


417
00:23:06,000 --> 00:23:08,000


418
00:23:08,000 --> 00:23:09,000


419
00:23:09,000 --> 00:23:13,000


420
00:23:13,000 --> 00:23:15,000


421
00:23:15,000 --> 00:23:17,000


422
00:23:17,000 --> 00:23:22,000


423
00:23:22,000 --> 00:23:25,000


424
00:23:25,000 --> 00:23:27,000


425
00:23:27,000 --> 00:23:29,000


426
00:23:29,000 --> 00:23:31,000


427
00:23:31,000 --> 00:23:37,000


428
00:23:37,000 --> 00:23:42,000


429
00:23:42,000 --> 00:23:43,000


430
00:23:43,000 --> 00:23:48,000


431
00:23:48,000 --> 00:23:49,000


432
00:23:49,000 --> 00:23:51,000


433
00:23:51,000 --> 00:23:55,000


434
00:23:55,000 --> 00:23:57,000


435
00:23:57,000 --> 00:23:59,000


436
00:23:59,000 --> 00:24:01,000


437
00:24:01,000 --> 00:24:04,000


438
00:24:04,000 --> 00:24:07,000


439
00:24:07,000 --> 00:24:10,000


440
00:24:10,000 --> 00:24:15,000


441
00:24:15,000 --> 00:24:16,000


442
00:24:16,000 --> 00:24:19,000


443
00:24:19,000 --> 00:24:20,000


444
00:24:20,000 --> 00:24:24,000


445
00:24:24,000 --> 00:24:29,000


446
00:24:29,000 --> 00:24:31,000


447
00:24:31,000 --> 00:24:37,000


448
00:24:37,000 --> 00:24:42,000


449
00:24:42,000 --> 00:24:44,000


450
00:24:44,000 --> 00:24:45,000


451
00:24:45,000 --> 00:24:49,000


452
00:24:49,000 --> 00:24:51,000


453
00:24:51,000 --> 00:24:52,000


454
00:24:52,000 --> 00:24:54,000


455
00:24:54,000 --> 00:24:58,000


456
00:24:58,000 --> 00:25:00,000


457
00:25:00,000 --> 00:25:01,000


458
00:25:01,000 --> 00:25:03,000


459
00:25:03,000 --> 00:25:07,000


460
00:25:07,000 --> 00:25:08,000


461
00:25:08,000 --> 00:25:13,000


462
00:25:13,000 --> 00:25:14,000


463
00:25:14,000 --> 00:25:18,000


464
00:25:18,000 --> 00:25:23,000


465
00:25:23,000 --> 00:25:24,000


466
00:25:24,000 --> 00:25:27,000


467
00:25:27,000 --> 00:25:32,000


468
00:25:32,000 --> 00:25:34,000


469
00:25:34,000 --> 00:25:37,000


470
00:25:37,000 --> 00:25:40,000


471
00:25:40,000 --> 00:25:45,000


472
00:25:45,000 --> 00:25:52,000


473
00:25:56,000 --> 00:25:58,000


474
00:25:58,000 --> 00:25:59,000


475
00:25:59,000 --> 00:26:01,000


476
00:26:01,000 --> 00:26:06,000


477
00:26:06,000 --> 00:26:09,000


478
00:26:09,000 --> 00:26:15,000


479
00:26:15,000 --> 00:26:15,000


480
00:26:15,000 --> 00:26:17,000


481
00:26:17,000 --> 00:26:21,000


482
00:26:21,000 --> 00:26:23,000


483
00:26:23,000 --> 00:26:27,000


484
00:26:27,000 --> 00:26:29,000


485
00:26:29,000 --> 00:26:31,000


486
00:26:31,000 --> 00:26:36,000


487
00:26:36,000 --> 00:26:41,000


488
00:26:41,000 --> 00:26:43,000


489
00:26:43,000 --> 00:26:50,000


490
00:26:51,000 --> 00:26:53,000


491
00:26:53,000 --> 00:26:57,000


492
00:26:57,000 --> 00:26:59,000


493
00:26:59,000 --> 00:27:02,000


494
00:27:02,000 --> 00:27:04,000


495
00:27:04,000 --> 00:27:07,000


496
00:27:07,000 --> 00:27:11,000


497
00:27:11,000 --> 00:27:13,000


498
00:27:13,000 --> 00:27:17,000


499
00:27:17,000 --> 00:27:19,000


500
00:27:19,000 --> 00:27:20,000


501
00:27:20,000 --> 00:27:22,000


502
00:27:22,000 --> 00:27:25,000


503
00:27:25,000 --> 00:27:28,000


504
00:27:28,000 --> 00:27:32,000


505
00:27:32,000 --> 00:27:34,000


506
00:27:34,000 --> 00:27:36,000


507
00:27:36,000 --> 00:27:37,000


508
00:27:37,000 --> 00:27:39,000


509
00:27:39,000 --> 00:27:42,000


510
00:27:42,000 --> 00:27:44,000


511
00:27:44,000 --> 00:27:51,000


512
00:28:00,000 --> 00:28:05,000


513
00:28:05,000 --> 00:28:07,000


514
00:28:07,000 --> 00:28:10,000


515
00:28:10,000 --> 00:28:11,000


516
00:28:11,000 --> 00:28:15,000


517
00:28:15,000 --> 00:28:19,000


518
00:28:19,000 --> 00:28:22,000


519
00:28:22,000 --> 00:28:29,000


520
00:28:39,000 --> 00:28:43,000


521
00:28:43,000 --> 00:28:46,000


522
00:28:46,000 --> 00:28:50,000


523
00:28:50,000 --> 00:28:52,000


524
00:28:52,000 --> 00:28:56,000


525
00:28:56,000 --> 00:28:59,000


526
00:28:59,000 --> 00:29:02,000


527
00:29:02,000 --> 00:29:08,000


528
00:29:08,000 --> 00:29:10,000


529
00:29:10,000 --> 00:29:12,000


530
00:29:12,000 --> 00:29:14,000


531
00:29:14,000 --> 00:29:18,000


532
00:29:18,000 --> 00:29:21,000


533
00:29:21,000 --> 00:29:25,000


534
00:29:25,000 --> 00:29:26,000


535
00:29:26,000 --> 00:29:30,000


536
00:29:30,000 --> 00:29:32,000


537
00:29:32,000 --> 00:29:35,000


538
00:29:35,000 --> 00:29:39,000


539
00:29:39,000 --> 00:29:40,000


540
00:29:40,000 --> 00:29:41,000


541
00:29:41,000 --> 00:29:48,000


542
00:29:48,000 --> 00:29:51,000


543
00:29:51,000 --> 00:29:57,000


544
00:29:57,000 --> 00:30:01,000


545
00:30:01,000 --> 00:30:02,000


546
00:30:02,000 --> 00:30:07,000


547
00:30:07,000 --> 00:30:12,000


548
00:30:12,000 --> 00:30:13,000


549
00:30:13,000 --> 00:30:17,000


550
00:30:17,000 --> 00:30:19,000


551
00:30:19,000 --> 00:30:22,000


552
00:30:22,000 --> 00:30:23,000


553
00:30:23,000 --> 00:30:27,000


554
00:30:27,000 --> 00:30:31,000


555
00:30:31,000 --> 00:30:33,000


556
00:30:33,000 --> 00:30:34,000


557
00:30:34,000 --> 00:30:37,000


558
00:30:37,000 --> 00:30:39,000


559
00:30:39,000 --> 00:30:42,000


560
00:30:42,000 --> 00:30:46,000


561
00:30:46,000 --> 00:30:47,000


562
00:30:47,000 --> 00:30:50,000


563
00:30:50,000 --> 00:30:52,000


564
00:30:52,000 --> 00:30:56,000


565
00:30:56,000 --> 00:31:00,000


566
00:31:00,000 --> 00:31:02,000


567
00:31:02,000 --> 00:31:07,000


568
00:31:07,000 --> 00:31:09,000


569
00:31:09,000 --> 00:31:13,000


570
00:31:13,000 --> 00:31:14,000


571
00:31:14,000 --> 00:31:16,000


572
00:31:16,000 --> 00:31:18,000


573
00:31:18,000 --> 00:31:23,000


574
00:31:23,000 --> 00:31:26,000


575
00:31:26,000 --> 00:31:28,000


576
00:31:28,000 --> 00:31:30,000


577
00:31:30,000 --> 00:31:34,000


578
00:31:34,000 --> 00:31:36,000


579
00:31:36,000 --> 00:31:37,000


580
00:31:37,000 --> 00:31:41,000


581
00:31:41,000 --> 00:31:46,000


582
00:31:46,000 --> 00:31:48,000


583
00:31:48,000 --> 00:31:51,000


584
00:31:51,000 --> 00:31:55,000


585
00:31:55,000 --> 00:31:57,000


586
00:31:57,000 --> 00:31:59,000


587
00:31:59,000 --> 00:32:01,000


588
00:32:01,000 --> 00:32:03,000


589
00:32:03,000 --> 00:32:05,000


590
00:32:05,000 --> 00:32:09,000


591
00:32:09,000 --> 00:32:15,000


592
00:32:15,000 --> 00:32:22,000


593
00:32:22,000 --> 00:32:25,000


594
00:32:25,000 --> 00:32:31,000


595
00:32:31,000 --> 00:32:31,000


596
00:32:31,000 --> 00:32:34,000


597
00:32:34,000 --> 00:32:41,000


598
00:32:41,000 --> 00:32:47,000


599
00:32:47,000 --> 00:32:54,000


600
00:32:59,000 --> 00:33:04,000


601
00:33:04,000 --> 00:33:10,000


602
00:33:10,000 --> 00:33:11,000


603
00:33:11,000 --> 00:33:13,000


604
00:33:13,000 --> 00:33:20,000


605
00:33:29,000 --> 00:33:34,000


606
00:33:34,000 --> 00:33:37,000


607
00:33:37,000 --> 00:33:41,000


608
00:33:41,000 --> 00:33:46,000


609
00:33:46,000 --> 00:33:50,000


610
00:33:50,000 --> 00:33:53,000


611
00:33:53,000 --> 00:33:56,000


612
00:33:56,000 --> 00:33:58,000


613
00:33:58,000 --> 00:34:01,000


614
00:34:01,000 --> 00:34:04,000


615
00:34:04,000 --> 00:34:08,000


616
00:34:08,000 --> 00:34:11,000


617
00:34:11,000 --> 00:34:13,000


618
00:34:13,000 --> 00:34:16,000


619
00:34:16,000 --> 00:34:20,000


620
00:34:20,000 --> 00:34:24,000


621
00:34:24,000 --> 00:34:31,000


622
00:34:38,000 --> 00:34:43,000


623
00:34:43,000 --> 00:34:47,000


624
00:34:47,000 --> 00:34:50,000


625
00:34:50,000 --> 00:34:54,000


626
00:34:54,000 --> 00:34:56,000


627
00:34:56,000 --> 00:35:00,000


628
00:35:00,000 --> 00:35:03,000


629
00:35:03,000 --> 00:35:06,000


630
00:35:06,000 --> 00:35:08,000


631
00:35:08,000 --> 00:35:11,000


632
00:35:11,000 --> 00:35:15,000


633
00:35:15,000 --> 00:35:19,000


634
00:35:19,000 --> 00:35:21,000


635
00:35:21,000 --> 00:35:23,000


636
00:35:23,000 --> 00:35:26,000


637
00:35:26,000 --> 00:35:29,000


638
00:35:29,000 --> 00:35:33,000


639
00:35:33,000 --> 00:35:37,000


640
00:35:37,000 --> 00:35:39,000


641
00:35:39,000 --> 00:35:42,000


642
00:35:42,000 --> 00:35:45,000


643
00:35:45,000 --> 00:35:49,000


644
00:35:49,000 --> 00:35:52,000


645
00:35:52,000 --> 00:35:53,000


646
00:35:53,000 --> 00:35:57,000


647
00:35:57,000 --> 00:36:04,000


648
00:36:06,000 --> 00:36:07,000


649
00:36:07,000 --> 00:36:10,000


650
00:36:10,000 --> 00:36:12,000


651
00:36:12,000 --> 00:36:16,000


652
00:36:16,000 --> 00:36:18,000


653
00:36:18,000 --> 00:36:19,000


654
00:36:19,000 --> 00:36:23,000


655
00:36:23,000 --> 00:36:28,000


656
00:36:28,000 --> 00:36:30,000


657
00:36:30,000 --> 00:36:35,000


658
00:36:35,000 --> 00:36:37,000


659
00:36:37,000 --> 00:36:40,000


660
00:36:40,000 --> 00:36:44,000


661
00:36:44,000 --> 00:36:48,000


662
00:36:48,000 --> 00:36:54,000


663
00:36:54,000 --> 00:36:55,000


664
00:36:55,000 --> 00:36:58,000


665
00:36:58,000 --> 00:37:02,000


666
00:37:02,000 --> 00:37:05,000


667
00:37:05,000 --> 00:37:10,000


668
00:37:10,000 --> 00:37:14,000


669
00:37:14,000 --> 00:37:19,000


670
00:37:19,000 --> 00:37:23,000


671
00:37:23,000 --> 00:37:27,000


672
00:37:27,000 --> 00:37:29,000


673
00:37:29,000 --> 00:37:30,000


674
00:37:30,000 --> 00:37:37,000


675
00:37:40,000 --> 00:37:44,000


676
00:37:44,000 --> 00:37:48,000


677
00:37:48,000 --> 00:37:49,000


678
00:37:49,000 --> 00:37:54,000


679
00:37:54,000 --> 00:37:56,000


680
00:37:56,000 --> 00:38:00,000


681
00:38:00,000 --> 00:38:02,000


682
00:38:02,000 --> 00:38:06,000


683
00:38:06,000 --> 00:38:07,000


684
00:38:07,000 --> 00:38:11,000


685
00:38:11,000 --> 00:38:13,000


686
00:38:13,000 --> 00:38:16,000


687
00:38:16,000 --> 00:38:19,000


688
00:38:19,000 --> 00:38:22,000


689
00:38:22,000 --> 00:38:25,000


690
00:38:25,000 --> 00:38:27,000


691
00:38:27,000 --> 00:38:31,000


692
00:38:31,000 --> 00:38:34,000


693
00:38:34,000 --> 00:38:40,000


694
00:38:40,000 --> 00:38:43,000


695
00:38:43,000 --> 00:38:46,000


696
00:38:46,000 --> 00:38:49,000


697
00:38:49,000 --> 00:38:50,000


698
00:38:50,000 --> 00:38:52,000


699
00:38:52,000 --> 00:38:57,000


700
00:38:57,000 --> 00:39:00,000


701
00:39:00,000 --> 00:39:03,000


702
00:39:03,000 --> 00:39:08,000


703
00:39:08,000 --> 00:39:09,000


704
00:39:09,000 --> 00:39:16,000


705
00:39:36,000 --> 00:39:38,000


706
00:39:38,000 --> 00:39:41,000


707
00:39:41,000 --> 00:39:43,000


708
00:39:43,000 --> 00:39:48,000


709
00:39:48,000 --> 00:39:50,000


710
00:39:50,000 --> 00:39:54,000


711
00:39:54,000 --> 00:39:57,000


712
00:39:57,000 --> 00:40:04,000


713
00:40:04,000 --> 00:40:11,000


714
00:40:11,000 --> 00:40:14,000


715
00:40:14,000 --> 00:40:18,000


716
00:40:18,000 --> 00:40:22,000


717
00:40:22,000 --> 00:40:25,000


718
00:40:25,000 --> 00:40:28,000


719
00:40:28,000 --> 00:40:32,000


720
00:40:32,000 --> 00:40:36,000


721
00:40:36,000 --> 00:40:39,000


722
00:40:39,000 --> 00:40:42,000


723
00:40:42,000 --> 00:40:46,000


724
00:40:46,000 --> 00:40:49,000


725
00:40:49,000 --> 00:40:50,000


726
00:40:50,000 --> 00:40:57,000


727
00:40:59,000 --> 00:41:04,000


728
00:41:04,000 --> 00:41:10,000


729
00:41:10,000 --> 00:41:14,000


730
00:41:14,000 --> 00:41:20,000


731
00:41:20,000 --> 00:41:24,000


732
00:41:24,000 --> 00:41:26,000


733
00:41:26,000 --> 00:41:30,000


734
00:41:30,000 --> 00:41:32,000


735
00:41:32,000 --> 00:41:36,000


736
00:41:36,000 --> 00:41:40,000


737
00:41:40,000 --> 00:41:45,000


738
00:41:45,000 --> 00:41:47,000


739
00:41:47,000 --> 00:41:51,000


740
00:41:51,000 --> 00:41:55,000


741
00:41:55,000 --> 00:41:57,000


742
00:41:57,000 --> 00:42:00,000


743
00:42:00,000 --> 00:42:02,000


744
00:42:02,000 --> 00:42:05,000


745
00:42:05,000 --> 00:42:10,000


746
00:42:10,000 --> 00:42:13,000


747
00:42:13,000 --> 00:42:15,000


748
00:42:15,000 --> 00:42:18,000


749
00:42:18,000 --> 00:42:25,000


750
00:42:30,000 --> 00:42:31,000


751
00:42:31,000 --> 00:42:33,000


752
00:42:33,000 --> 00:42:36,000


753
00:42:36,000 --> 00:42:37,000


754
00:42:37,000 --> 00:42:40,000


755
00:42:40,000 --> 00:42:46,000


756
00:42:46,000 --> 00:42:53,000


757
00:42:58,000 --> 00:43:01,000


758
00:43:01,000 --> 00:43:02,000


759
00:43:02,000 --> 00:43:09,000


760
00:43:09,000 --> 00:43:13,000


761
00:43:13,000 --> 00:43:20,000


762
00:43:20,000 --> 00:43:27,000


763
00:43:27,000 --> 00:43:29,000


764
00:43:29,000 --> 00:43:33,000


765
00:43:33,000 --> 00:43:36,000


766
00:43:36,000 --> 00:43:39,000


767
00:43:39,000 --> 00:43:43,000


768
00:43:43,000 --> 00:43:46,000


769
00:43:46,000 --> 00:43:53,000


770
00:43:53,000 --> 00:43:57,000


771
00:43:57,000 --> 00:44:01,000


772
00:44:01,000 --> 00:44:06,000


773
00:44:06,000 --> 00:44:09,000


774
00:44:09,000 --> 00:44:14,000


775
00:44:14,000 --> 00:44:16,000


776
00:44:16,000 --> 00:44:23,000


777
00:44:24,000 --> 00:44:31,000


778
00:44:35,000 --> 00:44:38,000


779
00:44:38,000 --> 00:44:39,000


780
00:44:39,000 --> 00:44:43,000


781
00:44:43,000 --> 00:44:44,000


782
00:44:44,000 --> 00:44:47,000


783
00:44:47,000 --> 00:44:52,000


784
00:44:52,000 --> 00:44:53,000


785
00:44:53,000 --> 00:45:00,000


786
00:45:01,000 --> 00:45:08,000


787
00:45:12,000 --> 00:45:16,000


788
00:45:16,000 --> 00:45:21,000


789
00:45:21,000 --> 00:45:28,000


790
00:45:32,000 --> 00:45:39,000


791
00:45:46,000 --> 00:45:50,000


792
00:45:50,000 --> 00:45:53,000


793
00:45:53,000 --> 00:45:58,000


794
00:45:58,000 --> 00:46:02,000


795
00:46:02,000 --> 00:46:09,000


796
00:46:22,000 --> 00:46:26,000


797
00:46:26,000 --> 00:46:26,000


798
00:46:26,000 --> 00:46:33,000


799
00:46:33,000 --> 00:46:35,000


800
00:46:35,000 --> 00:46:42,000


801
00:46:43,000 --> 00:46:44,000


802
00:46:44,000 --> 00:46:48,000


803
00:46:48,000 --> 00:46:50,000


804
00:46:50,000 --> 00:46:55,000


805
00:46:55,000 --> 00:46:56,000


806
00:46:56,000 --> 00:46:58,000


807
00:46:58,000 --> 00:47:05,000


808
00:47:06,000 --> 00:47:09,000


809
00:47:09,000 --> 00:47:10,000


810
00:47:10,000 --> 00:47:12,000


811
00:47:12,000 --> 00:47:14,000


812
00:47:14,000 --> 00:47:17,000


813
00:47:17,000 --> 00:47:17,000


814
00:47:17,000 --> 00:47:21,000


815
00:47:21,000 --> 00:47:21,000


816
00:47:21,000 --> 00:47:26,000


817
00:47:26,000 --> 00:47:30,000


818
00:47:30,000 --> 00:47:33,000


819
00:47:33,000 --> 00:47:34,000


820
00:47:34,000 --> 00:47:39,000


821
00:47:39,000 --> 00:47:42,000


822
00:47:42,000 --> 00:47:45,000


823
00:47:45,000 --> 00:47:47,000


824
00:47:47,000 --> 00:47:51,000


825
00:47:51,000 --> 00:47:52,000


826
00:47:52,000 --> 00:47:59,000


827
00:48:43,000 --> 00:48:50,000


828
00:48:51,000 --> 00:48:52,000


829
00:48:52,000 --> 00:48:53,000


830
00:48:53,000 --> 00:48:55,000


831
00:48:55,000 --> 00:48:57,000


832
00:48:57,000 --> 00:49:01,000


833
00:49:01,000 --> 00:49:05,000


834
00:49:05,000 --> 00:49:09,000


835
00:49:09,000 --> 00:49:12,000


836
00:49:12,000 --> 00:49:14,000


837
00:49:14,000 --> 00:49:17,000


838
00:49:17,000 --> 00:49:19,000


839
00:49:19,000 --> 00:49:22,000


840
00:49:22,000 --> 00:49:29,000


841
00:49:33,000 --> 00:49:38,000


842
00:49:38,000 --> 00:49:43,000


843
00:49:43,000 --> 00:49:43,000


844
00:49:43,000 --> 00:49:46,000


845
00:49:46,000 --> 00:49:50,000


846
00:49:50,000 --> 00:49:53,000


847
00:49:53,000 --> 00:49:57,000


848
00:49:57,000 --> 00:50:01,000


849
00:50:01,000 --> 00:50:04,000


850
00:50:04,000 --> 00:50:07,000


851
00:50:07,000 --> 00:50:10,000


852
00:50:10,000 --> 00:50:14,000


853
00:50:14,000 --> 00:50:15,000


854
00:50:15,000 --> 00:50:18,000


855
00:50:18,000 --> 00:50:21,000


856
00:50:21,000 --> 00:50:25,000


857
00:50:25,000 --> 00:50:29,000


858
00:50:29,000 --> 00:50:34,000


859
00:50:34,000 --> 00:50:37,000


860
00:50:37,000 --> 00:50:40,000


861
00:50:40,000 --> 00:50:44,000


862
00:50:44,000 --> 00:50:50,000


863
00:50:50,000 --> 00:50:55,000


864
00:50:55,000 --> 00:50:59,000


865
00:50:59,000 --> 00:51:06,000


866
00:51:06,000 --> 00:51:08,000


867
00:51:08,000 --> 00:51:14,000


868
00:51:14,000 --> 00:51:15,000


869
00:51:15,000 --> 00:51:18,000


870
00:51:18,000 --> 00:51:21,000


871
00:51:21,000 --> 00:51:25,000


872
00:51:25,000 --> 00:51:26,000


873
00:51:26,000 --> 00:51:30,000


874
00:51:30,000 --> 00:51:31,000


875
00:51:31,000 --> 00:51:32,000


876
00:51:32,000 --> 00:51:36,000


877
00:51:36,000 --> 00:51:37,000


878
00:51:37,000 --> 00:51:41,000


879
00:51:41,000 --> 00:51:47,000


880
00:51:47,000 --> 00:51:51,000


881
00:51:51,000 --> 00:51:53,000


882
00:51:53,000 --> 00:51:55,000


883
00:51:55,000 --> 00:51:58,000


884
00:51:58,000 --> 00:52:02,000


885
00:52:02,000 --> 00:52:02,000


886
00:52:02,000 --> 00:52:04,000


887
00:52:04,000 --> 00:52:06,000


888
00:52:06,000 --> 00:52:11,000


889
00:52:11,000 --> 00:52:16,000


890
00:52:16,000 --> 00:52:17,000


891
00:52:17,000 --> 00:52:19,000


892
00:52:19,000 --> 00:52:21,000


893
00:52:21,000 --> 00:52:26,000


894
00:52:26,000 --> 00:52:31,000


895
00:52:31,000 --> 00:52:33,000


896
00:52:33,000 --> 00:52:39,000


897
00:52:39,000 --> 00:52:43,000


898
00:52:43,000 --> 00:52:45,000


899
00:52:45,000 --> 00:52:50,000


900
00:52:50,000 --> 00:52:52,000


901
00:52:52,000 --> 00:52:55,000


902
00:52:55,000 --> 00:52:56,000


903
00:52:56,000 --> 00:52:59,000


904
00:52:59,000 --> 00:53:01,000


905
00:53:01,000 --> 00:53:04,000


906
00:53:04,000 --> 00:53:07,000


907
00:53:07,000 --> 00:53:10,000


908
00:53:10,000 --> 00:53:13,000


909
00:53:13,000 --> 00:53:15,000


910
00:53:15,000 --> 00:53:22,000


911
00:53:26,000 --> 00:53:30,000


912
00:53:30,000 --> 00:53:31,000


913
00:53:31,000 --> 00:53:36,000


914
00:53:36,000 --> 00:53:38,000


915
00:53:38,000 --> 00:53:42,000


916
00:53:42,000 --> 00:53:47,000


917
00:53:47,000 --> 00:53:49,000


918
00:53:49,000 --> 00:53:51,000


919
00:53:51,000 --> 00:53:54,000


920
00:53:54,000 --> 00:53:57,000


921
00:53:57,000 --> 00:54:00,000


922
00:54:00,000 --> 00:54:01,000


923
00:54:01,000 --> 00:54:04,000


924
00:54:04,000 --> 00:54:05,000


925
00:54:05,000 --> 00:54:11,000


926
00:54:11,000 --> 00:54:13,000


927
00:54:13,000 --> 00:54:17,000


928
00:54:17,000 --> 00:54:20,000


929
00:54:20,000 --> 00:54:24,000


930
00:54:24,000 --> 00:54:27,000


931
00:54:27,000 --> 00:54:29,000


932
00:54:29,000 --> 00:54:33,000


933
00:54:33,000 --> 00:54:39,000


934
00:54:39,000 --> 00:54:41,000


935
00:54:41,000 --> 00:54:44,000


936
00:54:44,000 --> 00:54:47,000


937
00:54:47,000 --> 00:54:50,000


938
00:54:50,000 --> 00:54:52,000


939
00:54:52,000 --> 00:54:55,000


940
00:54:55,000 --> 00:54:59,000


941
00:54:59,000 --> 00:55:03,000


942
00:55:03,000 --> 00:55:08,000


943
00:55:08,000 --> 00:55:10,000


944
00:55:10,000 --> 00:55:12,000


945
00:55:12,000 --> 00:55:13,000


946
00:55:13,000 --> 00:55:17,000


947
00:55:17,000 --> 00:55:18,000


948
00:55:18,000 --> 00:55:20,000


949
00:55:20,000 --> 00:55:22,000


950
00:55:22,000 --> 00:55:25,000


951
00:55:25,000 --> 00:55:27,000


952
00:55:27,000 --> 00:55:32,000


953
00:55:32,000 --> 00:55:38,000


954
00:55:38,000 --> 00:55:41,000


955
00:55:41,000 --> 00:55:48,000


956
00:55:48,000 --> 00:55:50,000


957
00:55:50,000 --> 00:55:56,000


958
00:55:56,000 --> 00:55:58,000


959
00:55:58,000 --> 00:56:04,000


960
00:56:04,000 --> 00:56:05,000


961
00:56:05,000 --> 00:56:07,000


962
00:56:07,000 --> 00:56:09,000


963
00:56:09,000 --> 00:56:12,000


964
00:56:12,000 --> 00:56:15,000


965
00:56:15,000 --> 00:56:17,000


966
00:56:17,000 --> 00:56:19,000


967
00:56:19,000 --> 00:56:24,000


968
00:56:24,000 --> 00:56:27,000


969
00:56:27,000 --> 00:56:30,000


970
00:56:30,000 --> 00:56:35,000


971
00:56:35,000 --> 00:56:37,000


972
00:56:37,000 --> 00:56:38,000


973
00:56:38,000 --> 00:56:42,000


974
00:56:42,000 --> 00:56:43,000


975
00:56:43,000 --> 00:56:46,000


976
00:56:46,000 --> 00:56:49,000


977
00:56:49,000 --> 00:56:51,000


978
00:56:51,000 --> 00:56:52,000


979
00:56:52,000 --> 00:56:54,000


980
00:56:54,000 --> 00:56:55,000


981
00:56:55,000 --> 00:56:57,000


982
00:56:57,000 --> 00:56:58,000


983
00:56:58,000 --> 00:57:00,000


984
00:57:00,000 --> 00:57:02,000


985
00:57:02,000 --> 00:57:07,000


986
00:57:07,000 --> 00:57:08,000


987
00:57:08,000 --> 00:57:15,000


988
00:57:15,000 --> 00:57:17,000


989
00:57:17,000 --> 00:57:20,000


990
00:57:20,000 --> 00:57:25,000


991
00:57:25,000 --> 00:57:30,000


992
00:57:30,000 --> 00:57:33,000


993
00:57:33,000 --> 00:57:35,000


994
00:57:35,000 --> 00:57:38,000


995
00:57:38,000 --> 00:57:39,000


996
00:57:39,000 --> 00:57:42,000


997
00:57:42,000 --> 00:57:45,000


998
00:57:45,000 --> 00:57:47,000


999
00:57:47,000 --> 00:57:54,000


1000
00:57:56,000 --> 00:57:59,000


1001
00:57:59,000 --> 00:58:02,000


1002
00:58:02,000 --> 00:58:04,000


1003
00:58:04,000 --> 00:58:11,000


1004
00:58:11,000 --> 00:58:15,000


1005
00:58:15,000 --> 00:58:19,000


1006
00:58:19,000 --> 00:58:22,000


1007
00:58:22,000 --> 00:58:24,000


1008
00:58:24,000 --> 00:58:25,000


1009
00:58:25,000 --> 00:58:28,000


1010
00:58:28,000 --> 00:58:31,000


1011
00:58:31,000 --> 00:58:33,000


1012
00:58:33,000 --> 00:58:34,000


1013
00:58:34,000 --> 00:58:37,000


1014
00:58:37,000 --> 00:58:42,000


1015
00:58:42,000 --> 00:58:44,000


1016
00:58:44,000 --> 00:58:48,000


1017
00:58:48,000 --> 00:58:51,000


1018
00:58:51,000 --> 00:58:54,000


1019
00:58:54,000 --> 00:58:55,000


1020
00:58:55,000 --> 00:58:59,000


1021
00:58:59,000 --> 00:59:03,000


1022
00:59:03,000 --> 00:59:05,000


1023
00:59:05,000 --> 00:59:08,000


1024
00:59:08,000 --> 00:59:09,000


1025
00:59:09,000 --> 00:59:14,000


1026
00:59:14,000 --> 00:59:15,000


1027
00:59:15,000 --> 00:59:17,000


1028
00:59:17,000 --> 00:59:18,000


1029
00:59:18,000 --> 00:59:20,000


1030
00:59:20,000 --> 00:59:22,000


1031
00:59:22,000 --> 00:59:29,000


1032
00:59:31,000 --> 00:59:35,000


1033
00:59:35,000 --> 00:59:38,000


1034
00:59:38,000 --> 00:59:41,000


1035
00:59:41,000 --> 00:59:46,000


1036
00:59:46,000 --> 00:59:48,000


1037
00:59:48,000 --> 00:59:50,000


1038
00:59:50,000 --> 00:59:54,000


1039
00:59:54,000 --> 00:59:56,000


1040
00:59:56,000 --> 00:59:59,000


1041
00:59:59,000 --> 01:00:01,000


1042
01:00:01,000 --> 01:00:08,000


1043
01:00:09,000 --> 01:00:16,000


1044
01:00:17,000 --> 01:00:19,000


1045
01:00:19,000 --> 01:00:26,000


1046
01:00:29,000 --> 01:00:35,000


1047
01:00:35,000 --> 01:00:42,000


1048
01:00:51,000 --> 01:00:53,000


1049
01:00:53,000 --> 01:00:57,000


1050
01:00:57,000 --> 01:00:58,000


1051
01:00:58,000 --> 01:01:03,000


1052
01:01:03,000 --> 01:01:08,000


1053
01:01:08,000 --> 01:01:09,000


1054
01:01:09,000 --> 01:01:11,000


1055
01:01:11,000 --> 01:01:14,000


1056
01:01:14,000 --> 01:01:17,000


1057
01:01:17,000 --> 01:01:19,000


1058
01:01:19,000 --> 01:01:20,000


1059
01:01:20,000 --> 01:01:22,000


1060
01:01:22,000 --> 01:01:24,000


1061
01:01:24,000 --> 01:01:27,000


1062
01:01:27,000 --> 01:01:34,000


1063
01:01:58,000 --> 01:01:59,000


1064
01:01:59,000 --> 01:02:03,000


1065
01:02:03,000 --> 01:02:05,000


1066
01:02:05,000 --> 01:02:11,000


1067
01:02:11,000 --> 01:02:18,000


1068
01:02:21,000 --> 01:02:27,000


1069
01:02:27,000 --> 01:02:28,000


1070
01:02:28,000 --> 01:02:32,000


1071
01:02:32,000 --> 01:02:34,000


1072
01:02:34,000 --> 01:02:37,000


1073
01:02:37,000 --> 01:02:40,000


1074
01:02:40,000 --> 01:02:41,000


1075
01:02:41,000 --> 01:02:42,000


1076
01:02:42,000 --> 01:02:45,000


1077
01:02:45,000 --> 01:02:48,000


1078
01:02:48,000 --> 01:02:48,000


1079
01:02:48,000 --> 01:02:51,000


1080
01:02:51,000 --> 01:02:54,000


1081
01:02:54,000 --> 01:02:56,000


1082
01:02:56,000 --> 01:02:58,000


1083
01:02:58,000 --> 01:03:01,000


1084
01:03:01,000 --> 01:03:03,000


1085
01:03:03,000 --> 01:03:08,000


1086
01:03:08,000 --> 01:03:09,000


1087
01:03:09,000 --> 01:03:13,000


1088
01:03:13,000 --> 01:03:14,000


1089
01:03:14,000 --> 01:03:16,000


1090
01:03:16,000 --> 01:03:20,000


1091
01:03:20,000 --> 01:03:23,000


1092
01:03:23,000 --> 01:03:24,000


1093
01:03:24,000 --> 01:03:25,000


1094
01:03:25,000 --> 01:03:27,000


1095
01:03:27,000 --> 01:03:32,000


1096
01:03:32,000 --> 01:03:35,000


1097
01:03:35,000 --> 01:03:36,000


1098
01:03:36,000 --> 01:03:41,000


1099
01:03:41,000 --> 01:03:43,000


1100
01:03:43,000 --> 01:03:48,000


1101
01:03:48,000 --> 01:03:52,000


1102
01:03:52,000 --> 01:03:58,000


1103
01:03:58,000 --> 01:04:00,000


1104
01:04:00,000 --> 01:04:02,000


1105
01:04:02,000 --> 01:04:09,000


1106
01:04:09,000 --> 01:04:13,000


1107
01:04:13,000 --> 01:04:17,000


1108
01:04:17,000 --> 01:04:19,000


1109
01:04:19,000 --> 01:04:21,000


1110
01:04:21,000 --> 01:04:27,000


1111
01:04:27,000 --> 01:04:29,000


1112
01:04:29,000 --> 01:04:36,000


1113
01:04:45,000 --> 01:04:47,000


1114
01:04:47,000 --> 01:04:50,000


1115
01:04:50,000 --> 01:04:53,000


1116
01:04:53,000 --> 01:04:55,000


1117
01:04:55,000 --> 01:04:57,000


1118
01:04:57,000 --> 01:04:58,000


1119
01:04:58,000 --> 01:05:00,000


1120
01:05:00,000 --> 01:05:04,000


1121
01:05:04,000 --> 01:05:06,000


1122
01:05:06,000 --> 01:05:08,000


1123
01:05:08,000 --> 01:05:13,000


1124
01:05:13,000 --> 01:05:15,000


1125
01:05:15,000 --> 01:05:19,000


1126
01:05:19,000 --> 01:05:21,000


1127
01:05:21,000 --> 01:05:24,000


1128
01:05:24,000 --> 01:05:25,000


1129
01:05:25,000 --> 01:05:27,000


1130
01:05:27,000 --> 01:05:34,000


1131
01:05:46,000 --> 01:05:50,000


1132
01:05:50,000 --> 01:05:56,000


1133
01:05:56,000 --> 01:05:59,000


1134
01:05:59,000 --> 01:06:02,000


1135
01:06:02,000 --> 01:06:06,000


1136
01:06:06,000 --> 01:06:07,000


1137
01:06:07,000 --> 01:06:11,000


1138
01:06:11,000 --> 01:06:13,000


1139
01:06:13,000 --> 01:06:19,000


1140
01:06:19,000 --> 01:06:23,000


1141
01:06:23,000 --> 01:06:25,000


1142
01:06:25,000 --> 01:06:30,000


1143
01:06:30,000 --> 01:06:33,000


1144
01:06:33,000 --> 01:06:40,000


1145
01:06:44,000 --> 01:06:46,000


1146
01:06:46,000 --> 01:06:48,000


1147
01:06:48,000 --> 01:06:49,000


1148
01:06:49,000 --> 01:06:51,000


1149
01:06:51,000 --> 01:06:54,000


1150
01:06:54,000 --> 01:06:55,000


1151
01:06:55,000 --> 01:06:58,000


1152
01:06:58,000 --> 01:06:59,000


1153
01:06:59,000 --> 01:07:03,000


1154
01:07:03,000 --> 01:07:06,000


1155
01:07:06,000 --> 01:07:09,000


1156
01:07:09,000 --> 01:07:11,000


1157
01:07:11,000 --> 01:07:14,000


1158
01:07:14,000 --> 01:07:17,000


1159
01:07:17,000 --> 01:07:20,000


1160
01:07:20,000 --> 01:07:23,000


1161
01:07:23,000 --> 01:07:25,000


1162
01:07:25,000 --> 01:07:29,000


1163
01:07:29,000 --> 01:07:32,000


1164
01:07:32,000 --> 01:07:34,000


1165
01:07:34,000 --> 01:07:35,000


1166
01:07:35,000 --> 01:07:39,000


1167
01:07:39,000 --> 01:07:42,000


1168
01:07:42,000 --> 01:07:44,000


1169
01:07:44,000 --> 01:07:48,000


1170
01:07:48,000 --> 01:07:51,000


1171
01:07:51,000 --> 01:07:56,000


1172
01:07:56,000 --> 01:07:59,000


1173
01:07:59,000 --> 01:08:01,000


1174
01:08:01,000 --> 01:08:02,000


1175
01:08:02,000 --> 01:08:05,000


1176
01:08:05,000 --> 01:08:06,000


1177
01:08:06,000 --> 01:08:09,000


1178
01:08:09,000 --> 01:08:13,000


1179
01:08:13,000 --> 01:08:14,000


1180
01:08:14,000 --> 01:08:18,000


1181
01:08:18,000 --> 01:08:20,000


1182
01:08:20,000 --> 01:08:23,000


1183
01:08:23,000 --> 01:08:26,000


1184
01:08:26,000 --> 01:08:32,000


1185
01:08:32,000 --> 01:08:36,000


1186
01:08:36,000 --> 01:08:40,000


1187
01:08:40,000 --> 01:08:41,000


1188
01:08:41,000 --> 01:08:44,000


1189
01:08:44,000 --> 01:08:45,000


1190
01:08:45,000 --> 01:08:49,000


1191
01:08:49,000 --> 01:08:53,000


1192
01:08:53,000 --> 01:08:55,000


1193
01:08:55,000 --> 01:08:57,000


1194
01:08:57,000 --> 01:09:02,000


1195
01:09:02,000 --> 01:09:06,000


1196
01:09:06,000 --> 01:09:09,000


1197
01:09:09,000 --> 01:09:11,000


1198
01:09:11,000 --> 01:09:14,000


1199
01:09:14,000 --> 01:09:17,000


1200
01:09:17,000 --> 01:09:19,000


1201
01:09:19,000 --> 01:09:22,000


1202
01:09:22,000 --> 01:09:26,000


1203
01:09:26,000 --> 01:09:28,000


1204
01:09:28,000 --> 01:09:32,000


1205
01:09:32,000 --> 01:09:36,000


1206
01:09:36,000 --> 01:09:39,000


1207
01:09:39,000 --> 01:09:46,000


1208
01:09:55,000 --> 01:09:58,000


1209
01:09:58,000 --> 01:09:59,000


1210
01:09:59,000 --> 01:10:01,000


1211
01:10:01,000 --> 01:10:08,000


1212
01:10:09,000 --> 01:10:11,000


1213
01:10:11,000 --> 01:10:13,000


1214
01:10:13,000 --> 01:10:13,000


1215
01:10:13,000 --> 01:10:16,000


1216
01:10:16,000 --> 01:10:20,000


1217
01:10:20,000 --> 01:10:23,000


1218
01:10:23,000 --> 01:10:27,000


1219
01:10:27,000 --> 01:10:27,000


1220
01:10:27,000 --> 01:10:30,000


1221
01:10:30,000 --> 01:10:32,000


1222
01:10:32,000 --> 01:10:37,000


1223
01:10:37,000 --> 01:10:38,000


1224
01:10:38,000 --> 01:10:39,000


1225
01:10:39,000 --> 01:10:41,000


1226
01:10:41,000 --> 01:10:43,000


1227
01:10:43,000 --> 01:10:46,000


1228
01:10:46,000 --> 01:10:48,000


1229
01:10:48,000 --> 01:10:52,000


1230
01:10:52,000 --> 01:10:54,000


1231
01:10:54,000 --> 01:11:01,000


1232
01:11:02,000 --> 01:11:07,000


1233
01:11:07,000 --> 01:11:13,000


1234
01:11:13,000 --> 01:11:14,000


1235
01:11:14,000 --> 01:11:17,000


1236
01:11:17,000 --> 01:11:20,000


1237
01:11:20,000 --> 01:11:21,000


1238
01:11:21,000 --> 01:11:24,000


1239
01:11:24,000 --> 01:11:28,000


1240
01:11:28,000 --> 01:11:30,000


1241
01:11:30,000 --> 01:11:32,000


1242
01:11:32,000 --> 01:11:39,000


1243
01:11:44,000 --> 01:11:49,000


1244
01:11:49,000 --> 01:11:51,000


1245
01:11:51,000 --> 01:11:53,000


1246
01:11:53,000 --> 01:11:56,000


1247
01:11:56,000 --> 01:12:00,000


1248
01:12:00,000 --> 01:12:01,000


1249
01:12:01,000 --> 01:12:05,000


1250
01:12:05,000 --> 01:12:09,000


1251
01:12:09,000 --> 01:12:11,000


1252
01:12:11,000 --> 01:12:18,000


1253
01:12:19,000 --> 01:12:23,000


1254
01:12:23,000 --> 01:12:25,000


1255
01:12:25,000 --> 01:12:28,000


1256
01:12:28,000 --> 01:12:31,000


1257
01:12:31,000 --> 01:12:33,000


1258
01:12:33,000 --> 01:12:36,000


1259
01:12:36,000 --> 01:12:41,000


1260
01:12:41,000 --> 01:12:42,000


1261
01:12:42,000 --> 01:12:45,000


1262
01:12:45,000 --> 01:12:49,000


1263
01:12:49,000 --> 01:12:54,000


1264
01:12:54,000 --> 01:12:56,000