├── README.md ├── RNN_Meetup_Pres_II.pdf ├── RNN_Meetup_Presentation.pdf ├── green_eggs_and_ham.txt ├── lstm.py ├── man_to_moon.txt ├── min-char-rnn.py ├── mlp.py ├── mlp_soln.py ├── one_fish_two_fish.txt ├── rnn.py ├── rnn_soln.py ├── run_lstm.py ├── sinwave.csv └── sp500.csv /README.md: -------------------------------------------------------------------------------- 1 | # Recurrent Neural Networks for Stock Price Prediction 2 | 3 | At the Meetup, we'll be using Python 3 and `numpy` for about 2/3 of the evening. The last third will use Keras. You can use either the Theano or Tensorflow backend. 4 | We'll be working out of a text editor and the IPython console. Starter scripts are found in this repo, and they are written in such a way that you could put blocks in Jupyter Notebooks and evaluate them there instead. 5 | 6 | ## Keras install instructions 7 | * Install keras using the command `pip install keras` 8 | 9 | * In the IPython shell, type `import keras`. It will fail (it can't find the `tensorflow` backend), but the whole point is to create the file at `~/.keras/keras.json` that you need to edit to use the `theano` backend if you are using Theano. Read about that [here.](https://keras.io/backend/) 10 | 11 | This talk is based on resources from [iamtrask](http://iamtrask.github.io/), [Andrey Karpathy](http://cs231n.stanford.edu/2016/syllabus), and [Jakob Aungiers.](http://www.jakob-aungiers.com/articles/a/LSTM-Neural-Network-for-Time-Series-Prediction) Many of the scripts in this repo originated from their blogs, repos, and Github pages and you'll find a more detailed citation in the script. Most scripts have been modified - all errors induced (or fixed!) are my own. 12 | 13 | -------------------------------------------------------------------------------- /RNN_Meetup_Pres_II.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GalvanizeOpenSource/Recurrent_Neural_Net_Meetup/ef421ab9fbcee72f2c26619f3c486952b9b8c06e/RNN_Meetup_Pres_II.pdf -------------------------------------------------------------------------------- /RNN_Meetup_Presentation.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GalvanizeOpenSource/Recurrent_Neural_Net_Meetup/ef421ab9fbcee72f2c26619f3c486952b9b8c06e/RNN_Meetup_Presentation.pdf -------------------------------------------------------------------------------- /green_eggs_and_ham.txt: -------------------------------------------------------------------------------- 1 | I am Daniel 2 | I am Sam 3 | Sam I am 4 | That Sam-I-am 5 | That Sam-I-am! 6 | I do not like 7 | That Sam-I-am 8 | Do you like 9 | Green eggs and ham 10 | I do not like them, 11 | Sam-I-am. 12 | I do not like 13 | Green eggs and ham. 14 | Would you like them 15 | Here or there? 16 | I would not like them 17 | Here or there. 18 | I would not like them 19 | Anywhere. 20 | I do not like 21 | Green eggs and ham. 22 | I do not like them, 23 | Sam-I-am 24 | Would you like them 25 | In a house? 26 | Would you like them 27 | With a mouse? 28 | I do not like them 29 | In a house. 30 | I do not like them 31 | With a mouse. 32 | I do not like them 33 | Here or there. 34 | I do not like them 35 | Anywhere. 36 | I do not like green eggs and ham. 37 | I do not like them, Sam-I-am. 38 | Would you eat them 39 | In a box? 40 | Would you eat them 41 | With a fox? 42 | Not in a box. 43 | Not with a fox. 44 | Not in a house. 45 | Not with a mouse. 46 | I would not eat them here or there. 47 | I would not eat them anywhere. 48 | I would not eat green eggs and ham. 49 | I do not like them, Sam-I-am. 50 | Would you? Could you? 51 | In a car? 52 | Eat them! Eat them! 53 | Here they are. 54 | I would not, 55 | Could not, 56 | In a car 57 | You may like them. 58 | You will see. 59 | You may like them 60 | In a tree? 61 | I would not, could not in a tree. 62 | Not in a car! You let me be. 63 | I do not like them in a box. 64 | I do not like them with a fox 65 | I do not like them in a house 66 | I do not like them with a mouse 67 | I do not like them here or there. 68 | I do not like them anywhere. 69 | I do not like green eggs and ham. 70 | I do not like them, Sam-I-am. 71 | A train! A train! 72 | A train! A train! 73 | Could you, would you 74 | On a train? 75 | Not on a train! Not in a tree! 76 | Not in a car! Sam! Let me be! 77 | I would not, could not, in a box. 78 | I could not, would not, with a fox. 79 | I will not eat them with a mouse 80 | I will not eat them in a house. 81 | I will not eat them here or there. 82 | I will not eat them anywhere. 83 | I do not like them, Sam-I-am. 84 | Say! 85 | In the dark? 86 | Here in the dark! 87 | Would you, could you, in the dark? 88 | I would not, could not, 89 | In the dark. 90 | Would you, could you, 91 | In the rain? 92 | I would not, could not, in the rain. 93 | Not in the dark. Not on a train, 94 | Not in a car, Not in a tree. 95 | I do not like them, Sam, you see. 96 | Not in a house. Not in a box. 97 | Not with a mouse. Not with a fox. 98 | I will not eat them here or there. 99 | I do not like them anywhere! 100 | You do not like 101 | Green eggs and ham? 102 | I do not 103 | Like them, 104 | Sam-I-am. 105 | Could you, would you, 106 | With a goat? 107 | I would not, 108 | Could not. 109 | With a goat! 110 | Would you, could you, 111 | On a boat? 112 | I could not, would not, on a boat. 113 | I will not, will not, with a goat. 114 | I will not eat them in the rain. 115 | I will not eat them on a train. 116 | Not in the dark! Not in a tree! 117 | Not in a car! You let me be! 118 | I do not like them in a box. 119 | I do not like them with a fox. 120 | I will not eat them in a house. 121 | I do not like them with a mouse. 122 | I do not like them here or there. 123 | I do not like them anywhere! 124 | I do not like 125 | Green egss 126 | And ham! 127 | I do not like them, 128 | Sam-I-am. 129 | You do not like them. 130 | So you say. 131 | Try them! Try them! 132 | And you may. 133 | Try them and you may I say. 134 | Sam! 135 | If you will let me be, 136 | I will try them. 137 | You will see. 138 | Say! 139 | I like green eggs and ham! 140 | I do! I like them, Sam-I-am! 141 | And I would eat them in a boat! 142 | And I would eat them with a goat. 143 | And I will eat them in the rain. 144 | And in the dark. And on a train. 145 | And in a car. And in a tree. 146 | They are so good so good you see! 147 | So I will eat them in a box. 148 | And I will eat them with a fox. 149 | And I will eat them in a house. 150 | And I will eat them with a mouse. 151 | And I will eat them here and there. 152 | Say! I will eat them anywhere! 153 | I do so like 154 | Green eggs and ham! 155 | Thank you! 156 | Thank you, 157 | Sam-I-am 158 | -------------------------------------------------------------------------------- /lstm.py: -------------------------------------------------------------------------------- 1 | import os 2 | import time 3 | import warnings 4 | import numpy as np 5 | from numpy import newaxis 6 | from keras.layers.core import Dense, Activation, Dropout 7 | from keras.layers.recurrent import LSTM 8 | from keras.models import Sequential 9 | 10 | #os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' #Hide messy TensorFlow warnings 11 | warnings.filterwarnings("ignore") #Hide messy Numpy warnings 12 | 13 | def load_data(filename, seq_len, normalise_window): 14 | f = open(filename, 'rb').read() 15 | data = f.decode().split('\n') 16 | 17 | sequence_length = seq_len + 1 18 | result = [] 19 | for index in range(len(data) - sequence_length): 20 | result.append(data[index: index + sequence_length]) 21 | 22 | if normalise_window: 23 | result = normalise_windows(result) 24 | 25 | result = np.array(result) 26 | row = round(0.9 * result.shape[0]) 27 | train = result[:int(row), :] 28 | np.random.shuffle(train) 29 | x_train = train[:, :-1] 30 | y_train = train[:, -1] 31 | x_test = result[int(row):, :-1] 32 | y_test = result[int(row):, -1] 33 | 34 | x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1)) 35 | x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1)) 36 | 37 | return [x_train, y_train, x_test, y_test] 38 | 39 | def normalise_windows(window_data): 40 | normalised_data = [] 41 | for window in window_data: 42 | normalised_window = [((float(p) / float(window[0])) - 1) for p in window] 43 | normalised_data.append(normalised_window) 44 | return normalised_data 45 | 46 | def build_model(layers): 47 | model = Sequential() 48 | 49 | model.add(LSTM( 50 | input_shape=(layers[1], layers[0]), 51 | output_dim=layers[1], 52 | return_sequences=True)) 53 | model.add(Activation("tanh")) 54 | model.add(Dropout(0.2)) 55 | 56 | model.add(LSTM( 57 | layers[2], 58 | return_sequences=False)) 59 | #model.add(Activation("tanh")) 60 | model.add(Dropout(0.2)) 61 | 62 | model.add(Dense( 63 | output_dim=layers[3])) 64 | model.add(Activation("linear")) 65 | 66 | start = time.time() 67 | model.compile(loss="mse", optimizer="rmsprop") 68 | print("> Compilation Time : ", time.time() - start) 69 | return model 70 | 71 | def predict_point_by_point(model, data): 72 | #Predict each timestep given the last sequence of true data, in effect only predicting 1 step ahead each time 73 | predicted = model.predict(data) 74 | predicted = np.reshape(predicted, (predicted.size,)) 75 | return predicted 76 | 77 | def predict_sequence_full(model, data, window_size): 78 | #Shift the window by 1 new prediction each time, re-run predictions on new window 79 | curr_frame = data[0] 80 | predicted = [] 81 | for i in range(len(data)): 82 | predicted.append(model.predict(curr_frame[newaxis,:,:])[0,0]) 83 | curr_frame = curr_frame[1:] 84 | curr_frame = np.insert(curr_frame, [window_size-1], predicted[-1], axis=0) 85 | return predicted 86 | 87 | def predict_sequences_multiple(model, data, window_size, prediction_len): 88 | #Predict sequence of 50 steps before shifting prediction run forward by 50 steps 89 | prediction_seqs = [] 90 | for i in range(int(len(data)/prediction_len)): 91 | curr_frame = data[i*prediction_len] 92 | predicted = [] 93 | for j in range(prediction_len): 94 | predicted.append(model.predict(curr_frame[newaxis,:,:])[0,0]) 95 | curr_frame = curr_frame[1:] 96 | curr_frame = np.insert(curr_frame, [window_size-1], predicted[-1], axis=0) 97 | prediction_seqs.append(predicted) 98 | return prediction_seqs 99 | -------------------------------------------------------------------------------- /man_to_moon.txt: -------------------------------------------------------------------------------- 1 | President Pitzer, Mr. Vice President, Governor, Congressman Thomas, Senator Wiley, and Congressman Miller, Mr. Webb, Mr. Bell, scientists, distinguished guests, and ladies and gentlemen: 2 | 3 | I appreciate your president having made me an honorary visiting professor, and I will assure you that my first lecture will be very brief. 4 | 5 | I am delighted to be here and I'm particularly delighted to be here on this occasion. 6 | 7 | We meet at a college noted for knowledge, in a city noted for progress, in a state noted for strength, and we stand in need of all three, for we meet in an hour of change and challenge, in a decade of hope and fear, in an age of both knowledge and ignorance. The greater our knowledge increases, the greater our ignorance unfolds. 8 | 9 | Despite the striking fact that most of the scientists that the world has ever known are alive and working today, despite the fact that this Nation's own scientific manpower is doubling every 12 years in a rate of growth more than three times that of our population as a whole, despite that, the vast stretches of the unknown and the unanswered and the unfinished still far outstrip our collective comprehension. 10 | 11 | No man can fully grasp how far and how fast we have come, but condense, if you will, the 50 thousand years of man's recorded history in a time span of but a half-century. Stated in these terms, we know very little about the first 40 years, except at the end of them advanced man had learned to use the skins of animals to cover them. Then about 10 years ago, under this standard, man emerged from his caves to construct other kinds of shelter. Only five years ago man learned to write and use a cart with wheels. Christianity began less than two years ago. The printing press came this year, and then less than two months ago, during this whole 50-year span of human history, the steam engine provided a new source of power. Newton explored the meaning of gravity. Last month electric lights and telephones and automobiles and airplanes became available. Only last week did we develop penicillin and television and nuclear power, and now if America's new spacecraft succeeds in reaching Venus, we will have literally reached the stars before midnight tonight. 12 | 13 | This is a breathtaking pace, and such a pace cannot help but create new ills as it dispels old, new ignorance, new problems, new dangers. Surely the opening vistas of space promise high costs and hardships, as well as high reward. 14 | 15 | So it is not surprising that some would have us stay where we are a little longer to rest, to wait. But this city of Houston, this state of Texas, this country of the United States was not built by those who waited and rested and wished to look behind them. This country was conquered by those who moved forward—and so will space. 16 | 17 | William Bradford, speaking in 1630 of the founding of the Plymouth Bay Colony, said that all great and honorable actions are accompanied with great difficulties, and both must be enterprised and overcome with answerable courage. 18 | 19 | If this capsule history of our progress teaches us anything, it is that man, in his quest for knowledge and progress, is determined and cannot be deterred. The exploration of space will go ahead, whether we join in it or not, and it is one of the great adventures of all time, and no nation which expects to be the leader of other nations can expect to stay behind in this race for space. 20 | 21 | Those who came before us made certain that this country rode the first waves of the industrial revolution, the first waves of modern invention, and the first wave of nuclear power, and this generation does not intend to founder in the backwash of the coming age of space. We mean to be a part of it—we mean to lead it. For the eyes of the world now look into space, to the moon and to the planets beyond, and we have vowed that we shall not see it governed by a hostile flag of conquest, but by a banner of freedom and peace. We have vowed that we shall not see space filled with weapons of mass destruction, but with instruments of knowledge and understanding. 22 | 23 | Yet the vows of this Nation can only be fulfilled if we in this Nation are first, and, therefore, we intend to be first. In short, our leadership in science and industry, our hopes for peace and security, our obligations to ourselves as well as others, all require us to make this effort, to solve these mysteries, to solve them for the good of all men, and to become the world's leading space-faring nation. 24 | 25 | We set sail on this new sea because there is new knowledge to be gained, and new rights to be won, and they must be won and used for the progress of all people. For space science, like nuclear science and all technology, has no conscience of its own. Whether it will become a force for good or ill depends on man, and only if the United States occupies a position of pre-eminence can we help decide whether this new ocean will be a sea of peace or a new terrifying theater of war. I do not say that we should or will go unprotected against the hostile misuse of space any more than we go unprotected against the hostile use of land or sea, but I do say that space can be explored and mastered without feeding the fires of war, without repeating the mistakes that man has made in extending his writ around this globe of ours. 26 | 27 | There is no strife, no prejudice, no national conflict in outer space as yet. Its hazards are hostile to us all. Its conquest deserves the best of all mankind, and its opportunity for peaceful cooperation may never come again. But why, some say, the moon? Why choose this as our goal? And they may well ask why climb the highest mountain? Why, 35 years ago, fly the Atlantic? Why does Rice play Texas? 28 | 29 | We choose to go to the moon. We choose to go to the moon... (interrupted by applause) we choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard, because that goal will serve to organize and measure the best of our energies and skills, because that challenge is one that we are willing to accept, one we are unwilling to postpone, and one which we intend to win, and the others, too. 30 | 31 | It is for these reasons that I regard the decision last year to shift our efforts in space from low to high gear as among the most important decisions that will be made during my incumbency in the office of the Presidency. 32 | 33 | In the last 24 hours we have seen facilities now being created for the greatest and most complex exploration in man's history. We have felt the ground shake and the air shattered by the testing of a Saturn C-1 booster rocket, many times as powerful as the Atlas which launched John Glenn, generating power equivalent to 10 thousand automobiles with their accelerators on the floor. We have seen the site where five F-1 rocket engines, each one as powerful as all eight engines of the Saturn combined, will be clustered together to make the advanced Saturn missile, assembled in a new building to be built at Cape Canaveral as tall as a 48 story structure, as wide as a city block, and as long as two lengths of this field. 34 | 35 | Within these last 19 months at least 45 satellites have circled the earth. Some 40 of them were made in the United States of America and they were far more sophisticated and supplied far more knowledge to the people of the world than those of the Soviet Union. 36 | 37 | The Mariner spacecraft... (interrupted by applause) the Mariner spacecraft now on its way to Venus is the most intricate instrument in the history of space science. The accuracy of that shot is comparable to firing a missile from Cape Canaveral and dropping it in this stadium between the 40-yard lines. 38 | 39 | Transit satellites are helping our ships at sea to steer a safer course. Tiros satellites have given us unprecedented warnings of hurricanes and storms, and will do the same for forest fires and icebergs. 40 | 41 | We have had our failures, but so have others, even if they do not admit them. And they may be less public. 42 | 43 | To be sure,... (interrupted by applause) to be sure, we are behind, and will be behind for some time in manned flight. But we do not intend to stay behind, and in this decade, we shall make up and move ahead. 44 | 45 | The growth of our science and education will be enriched by new knowledge of our universe and environment, by new techniques of learning and mapping and observation, by new tools and computers for industry, medicine, the home as well as the school. Technical institutions, such as Rice, will reap the harvest of these gains. 46 | 47 | And finally, the space effort itself, while still in its infancy, has already created a great number of new companies, and tens of thousands of new jobs. Space and related industries are generating new demands in investment and skilled personnel, and this city and this state, and this region, will share greatly in this growth. What was once the furthest outpost on the old frontier of the West will be the furthest outpost on the new frontier of science and space. Houston, (interrupted by applause) your city of Houston, with its Manned Spacecraft Center, will become the heart of a large scientific and engineering community. During the next 5 years the National Aeronautics and Space Administration expects to double the number of scientists and engineers in this area, to increase its outlays for salaries and expenses to 60 million dollars a year; to invest some 200 million dollars in plant and laboratory facilities; and to direct or contract for new space efforts over 1 billion dollars from this center in this city. 48 | 49 | To be sure, all this costs us all a good deal of money. This year's space budget is three times what it was in January 1961, and it is greater than the space budget of the previous eight years combined. That budget now stands at 5 billion 400 million dollars a year—a staggering sum, though somewhat less than we pay for cigarettes and cigars every year. Space expenditures will soon rise some more, from 40 cents per person per week to more than 50 cents a week for every man, woman and child in the United States, for we have given this program a high national priority—even though I realize that this is in some measure an act of faith and vision, for we do not now know what benefits await us. But if I were to say, my fellow citizens, that we shall send to the moon, 240 thousand miles away from the control station in Houston, a giant rocket more than 300 feet tall, the length of this football field, made of new metal alloys, some of which have not yet been invented, capable of standing heat and stresses several times more than have ever been experienced, fitted together with a precision better than the finest watch, carrying all the equipment needed for propulsion, guidance, control, communications, food and survival, on an untried mission, to an unknown celestial body, and then return it safely to earth, re-entering the atmosphere at speeds of over 25 thousand miles per hour, causing heat about half that of the temperature of the sun—almost as hot as it is here today—and do all this, and do it right, and do it first before this decade is out—then we must be bold. 50 | 51 | I'm the one who is doing all the work, so we just want you to stay cool for a minute. 52 | 53 | However, I think we're going to do it, and I think that we must pay what needs to be paid. I don't think we ought to waste any money, but I think we ought to do the job. And this will be done in the decade of the Sixties. It may be done while some of you are still here at school at this college and university. It will be done during the terms of office of some of the people who sit here on this platform. But it will be done. And it will be done before the end of this decade. 54 | 55 | And I am delighted that this university is playing a part in putting a man on the moon as part of a great national effort of the United States of America. 56 | 57 | Many years ago the great British explorer George Mallory, who was to die on Mount Everest, was asked why did he want to climb it. He said, "Because it is there." 58 | 59 | Well, space is there, and we're going to climb it, and the moon and the planets are there, and new hopes for knowledge and peace are there. And, therefore, as we set sail we ask God's blessing on the most hazardous and dangerous and greatest adventure on which man has ever embarked. 60 | 61 | Thank you. 62 | -------------------------------------------------------------------------------- /min-char-rnn.py: -------------------------------------------------------------------------------- 1 | """ 2 | Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) 3 | BSD License 4 | Code available at https://gist.github.com/karpathy/d4dee566867f8291f086 5 | Karpathy's youtube lecture where he goes through many parts of this code 6 | line-by-line: 7 | https://www.youtube.com/watch?v=yCC09vCHzF8 8 | """ 9 | import numpy as np 10 | 11 | # data I/O 12 | # file options: 'man_to_moon.txt', 'green_eggs_and_ham.txt', 'one_fish_two_fish.txt' 13 | data = open('green_eggs_and_ham.txt', 'r').read() # should be simple plain text file 14 | chars = list(set(data)) 15 | data_size, vocab_size = len(data), len(chars) 16 | print("data has {0} characters, {1} unique.".format(data_size, vocab_size)) 17 | char_to_ix = { ch:i for i,ch in enumerate(chars) } 18 | ix_to_char = { i:ch for i,ch in enumerate(chars) } 19 | 20 | # hyperparameters 21 | hidden_size = 100 # size of hidden layer of neurons 22 | seq_length = 25 # number of steps to unroll the RNN for 23 | learning_rate = 1e-1 24 | 25 | # model parameters 26 | Wxh = np.random.randn(hidden_size, vocab_size)*0.01 # input to hidden 27 | Whh = np.random.randn(hidden_size, hidden_size)*0.01 # hidden to hidden 28 | Why = np.random.randn(vocab_size, hidden_size)*0.01 # hidden to output 29 | bh = np.zeros((hidden_size, 1)) # hidden bias 30 | by = np.zeros((vocab_size, 1)) # output bias 31 | 32 | def lossFun(inputs, targets, hprev): 33 | """ 34 | inputs,targets are both list of integers. 35 | hprev is Hx1 array of initial hidden state 36 | returns the loss, gradients on model parameters, and last hidden state 37 | """ 38 | xs, hs, ys, ps = {}, {}, {}, {} 39 | hs[-1] = np.copy(hprev) 40 | loss = 0 41 | # forward pass 42 | for t in range(len(inputs)): 43 | xs[t] = np.zeros((vocab_size,1)) # encode in 1-of-k representation 44 | xs[t][inputs[t]] = 1 45 | hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state 46 | ys[t] = np.dot(Why, hs[t]) + by # unnormalized log probabilities for next chars 47 | ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t])) # probabilities for next chars 48 | loss += -np.log(ps[t][targets[t],0]) # softmax (cross-entropy loss) 49 | # backward pass: compute gradients going backwards 50 | dWxh, dWhh, dWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why) 51 | dbh, dby = np.zeros_like(bh), np.zeros_like(by) 52 | dhnext = np.zeros_like(hs[0]) 53 | for t in reversed(range(len(inputs))): 54 | dy = np.copy(ps[t]) 55 | dy[targets[t]] -= 1 # backprop into y. see http://cs231n.github.io/neural-networks-case-study/#grad if confused here 56 | dWhy += np.dot(dy, hs[t].T) 57 | dby += dy 58 | dh = np.dot(Why.T, dy) + dhnext # backprop into h 59 | dhraw = (1 - hs[t] * hs[t]) * dh # backprop through tanh nonlinearity 60 | dbh += dhraw 61 | dWxh += np.dot(dhraw, xs[t].T) 62 | dWhh += np.dot(dhraw, hs[t-1].T) 63 | dhnext = np.dot(Whh.T, dhraw) 64 | for dparam in [dWxh, dWhh, dWhy, dbh, dby]: 65 | np.clip(dparam, -5, 5, out=dparam) # clip to mitigate exploding gradients 66 | return loss, dWxh, dWhh, dWhy, dbh, dby, hs[len(inputs)-1] 67 | 68 | def sample(h, seed_ix, n): 69 | """ 70 | sample a sequence of integers from the model 71 | h is memory state, seed_ix is seed letter for first time step 72 | """ 73 | x = np.zeros((vocab_size, 1)) 74 | x[seed_ix] = 1 75 | ixes = [] 76 | for t in range(n): 77 | h = np.tanh(np.dot(Wxh, x) + np.dot(Whh, h) + bh) 78 | y = np.dot(Why, h) + by 79 | p = np.exp(y) / np.sum(np.exp(y)) 80 | ix = np.random.choice(range(vocab_size), p=p.ravel()) 81 | x = np.zeros((vocab_size, 1)) 82 | x[ix] = 1 83 | ixes.append(ix) 84 | return ixes 85 | 86 | n, p = 0, 0 87 | mWxh, mWhh, mWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why) 88 | mbh, mby = np.zeros_like(bh), np.zeros_like(by) # memory variables for Adagrad 89 | smooth_loss = -np.log(1.0/vocab_size)*seq_length # loss at iteration 0 90 | prnt_intvl = 1000 91 | while True: 92 | # prepare inputs (we're sweeping from left to right in steps seq_length long) 93 | if p+seq_length+1 >= len(data) or n == 0: 94 | hprev = np.zeros((hidden_size,1)) # reset RNN memory 95 | p = 0 # go from start of data 96 | inputs = [char_to_ix[ch] for ch in data[p:p+seq_length]] 97 | targets = [char_to_ix[ch] for ch in data[p+1:p+seq_length+1]] 98 | 99 | # sample from the model now and then 100 | if n % prnt_intvl == 0: 101 | sample_ix = sample(hprev, inputs[0], 200) 102 | txt = ''.join(ix_to_char[ix] for ix in sample_ix) 103 | print("----\n {0} \n----".format(txt)) 104 | 105 | # forward seq_length characters through the net and fetch gradient 106 | loss, dWxh, dWhh, dWhy, dbh, dby, hprev = lossFun(inputs, targets, hprev) 107 | smooth_loss = round(smooth_loss * 0.999 + loss * 0.001,3) 108 | if n % prnt_intvl == 0: 109 | print("iter {0}, loss: {1}".format(n, smooth_loss)) # print progress 110 | 111 | # perform parameter update with Adagrad 112 | for param, dparam, mem in zip([Wxh, Whh, Why, bh, by], 113 | [dWxh, dWhh, dWhy, dbh, dby], 114 | [mWxh, mWhh, mWhy, mbh, mby]): 115 | mem += dparam * dparam 116 | param += -learning_rate * dparam / np.sqrt(mem + 1e-8) # adagrad update 117 | 118 | p += seq_length # move data pointer 119 | n += 1 # iteration counter 120 | -------------------------------------------------------------------------------- /mlp.py: -------------------------------------------------------------------------------- 1 | # MLP 3 layer network: input - hidden - outer 2 | # based on: 3 | # http://iamtrask.github.io/2015/07/12/basic-python-network/ 4 | 5 | import numpy as np 6 | 7 | def activation(x,deriv=False): 8 | '''activation function, values for sigmoid''' 9 | if(deriv==True): 10 | return x*(1-x) # derivative of sigmoid* 11 | return 1/(1+np.exp(-x)) 12 | 13 | # inputs (X) 14 | inputs = np.array([[0,0], 15 | [0,1], 16 | [1,0], 17 | [1,1]]) 18 | 19 | # targets (y) - presently for AND, change for OR and XOR 20 | targets = np.array([[0], 21 | [0], 22 | [0], 23 | [1]]) 24 | 25 | 26 | ## make network geometry 27 | #nodes_input = 28 | #nodes_hidden = 29 | #nodes_target = 30 | 31 | ## initialize weights (-1.0 to 1.0) 32 | #Wxh = 2*np.random.uniform(size=( , )) - 1 33 | #Why = 2*np.random.uniform(size=( , )) - 1 34 | 35 | ## simulation parameters 36 | #np.random.seed(1) 37 | #alpha = 1 # learning rate 38 | #num_epochs = 39 | 40 | # training pseudo code 41 | # for each epoch: 42 | # for each row of X, y in inputs, targets 43 | # Feed forward to find values of: 44 | # H 45 | # yp (the prediction) 46 | 47 | # Back propogate to find the gradient of the loss with respect to: 48 | # Why 49 | # Wxh 50 | 51 | # Use gradient descent to update the weights 52 | 53 | # for this epoch, print training error 54 | 55 | # print final comparison between target and predictions 56 | -------------------------------------------------------------------------------- /mlp_soln.py: -------------------------------------------------------------------------------- 1 | # MLP 3 layer network: input - hidden - outer 2 | # based on: 3 | # http://iamtrask.github.io/2015/07/12/basic-python-network/ 4 | 5 | import numpy as np 6 | import pdb 7 | 8 | def activation(x,deriv=False): 9 | '''activation function, values for sigmoid''' 10 | if(deriv==True): 11 | return x*(1-x) # not technically correct 12 | return 1/(1+np.exp(-x)) 13 | 14 | # inputs 15 | inputs = np.array([[0,0], 16 | [0,1], 17 | [1,0], 18 | [1,1]]) 19 | # targets 20 | targets = np.array([[0], 21 | [1], 22 | [1], 23 | [0]]) 24 | 25 | 26 | # make network geometry 27 | nodes_input = 2 28 | nodes_hidden = 4 29 | nodes_target = 1 30 | 31 | # initialize weights 32 | Wxh = 2*np.random.uniform(size=(nodes_input, nodes_hidden)) - 1 33 | Why = 2*np.random.uniform(size=(nodes_hidden, nodes_target)) - 1 34 | 35 | # simulation parameters 36 | np.random.seed(1) 37 | alpha = 1 # learning rate 38 | num_epochs = 20000 # number of epochs 39 | 40 | # training 41 | print("\nTraining:") 42 | for e in range(num_epochs): 43 | yp_lst = [] # predictions 44 | error_lst = [] # differences between target and predictions 45 | for X, y in zip(inputs, targets): 46 | X = X.reshape((1, X.shape[0])) # for row, column shape 47 | # Feed forward 48 | H = activation(np.dot(X,Wxh)) 49 | yp = activation(np.dot(H,Why)) 50 | # Back propogate to find gradients 51 | # Why gradients 52 | yp_error = y - yp 53 | yp_delta = yp_error*activation(yp,deriv=True) 54 | grad_Why = np.dot(H.T, yp_delta) 55 | # Wxh gradients 56 | H_error = np.dot(yp_delta, Why.T) 57 | H_delta = H_error * activation(H,deriv=True) 58 | grad_Wxh = np.dot(X.T, H_delta) 59 | # Use gradient descent to update weights 60 | Why += alpha * grad_Why 61 | Wxh += alpha * grad_Wxh 62 | 63 | # save for future use 64 | yp_lst.append(yp[0][0]) 65 | error_lst.append(yp_error[0][0]) 66 | 67 | epoch_error = np.mean(np.abs(error_lst)).round(4) 68 | if (e % int(num_epochs/10)) == 0: 69 | print("Epoch: {0:<8s} Error: {1}".format(str(e), epoch_error)) 70 | 71 | print("\nResults:") 72 | print("yt\typ\typ_prob") 73 | for yt, yp in zip(targets,yp_lst): 74 | print("{0}\t{1}\t{2}".format(yt[0], int(yp>=0.5), round(yp,4))) 75 | -------------------------------------------------------------------------------- /one_fish_two_fish.txt: -------------------------------------------------------------------------------- 1 | One fish 2 | Two fish 3 | Red fish 4 | Blue fish. 5 | Black fish 6 | Blue fish 7 | Old fish 8 | New fish. 9 | This one has a little star. 10 | This one has a little car. 11 | Say! What a lot 12 | Of fish there are. 13 | Yes. Some are red. And some are blue. 14 | Some are old. And some are new. 15 | Some are sad. 16 | And some are glad. 17 | And some are very, very bad. 18 | Why are they 19 | Sad and glad and bad? 20 | I do not know. 21 | Go ask your dad. 22 | Some are thin. 23 | And some are fat. 24 | The fat one has 25 | A yellow hat. 26 | From there to here, from here to there, 27 | Funny things 28 | Are everywhere. 29 | Here are some 30 | Who like to run. 31 | They run for fun 32 | In the hot, hot sun. 33 | Oh me! Oh my! 34 | Oh me! Oh my! 35 | What a lot 36 | Of funny things go by. 37 | Some have two feet 38 | And some have four. 39 | Some have six feet 40 | And some have more. 41 | Where do they come from? I can’t say. 42 | But I bet they have come a long, long way. 43 | We see them come. 44 | We see them go. 45 | Some are fast. 46 | And some are slow. 47 | Some are high 48 | And some are low. 49 | Not one of them 50 | Is like another. 51 | Don’t ask us why. 52 | Go ask your mother. 53 | Say! 54 | Look at his fingers! 55 | One, two, three... 56 | How many fingers 57 | Do I see? 58 | One, two, three, four, 59 | Five, six, seven, 60 | Eight, nine, ten. 61 | He has eleven! 62 | Eleven! 63 | This is something new. 64 | I wish I had 65 | Eleven, too! 66 | -------------------------------------------------------------------------------- /rnn.py: -------------------------------------------------------------------------------- 1 | #Vanilla RNN predicts sine wave 2 | #based on iamtrask's blog: 3 | #http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/ 4 | #and code in jakob aungiers github: 5 | #https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction 6 | 7 | import numpy as np 8 | import matplotlib.pyplot as plt 9 | 10 | #plotting 11 | def plot_train_test(x, y, train_size): 12 | ''' for visualizing train-test split ''' 13 | split = int(len(x)*train_size) 14 | x_train, y_train = x[:split], y[:split] 15 | x_test, y_test = x[split:], y[split:] 16 | plt.plot(x_train, y_train, 'b-', label='train') 17 | plt.plot(x_test, y_test, 'r-', label='test') 18 | plt.legend() 19 | plt.show() 20 | 21 | def plot_predict_vs_test(y_pred, y_test, figtitle): 22 | ''' visualize peformance on test set ''' 23 | dps = np.arange(0,y_test.shape[0]) 24 | plt.plot(dps, y_test, '-b', label='test') 25 | plt.plot(dps, y_pred, '-g', label='prediction') 26 | plt.legend() 27 | plt.title(figtitle) 28 | plt.show() 29 | 30 | # activation function 31 | def activation(x,deriv=False): 32 | '''activation function, values for tanh''' 33 | if(deriv==True): 34 | return 1 - np.tanh(x)**2 35 | return np.tanh(x) 36 | 37 | # inputs 38 | def make_sine_wave(cycles, pts_per_cycle): 39 | ''' makes a sine wave, centered on 0, with peak-to-peak amplitude 40 | of 2 (-1 to 1), with the desired number of cycles and the number of 41 | points per cycle 42 | ''' 43 | x = np.linspace(0, cycles*2*np.pi, num=cycles*pts_per_cycle+1, endpoint=True) 44 | return x, np.sin(x) 45 | 46 | 47 | def make_train_test_seqs(sequence, seq_len, train_size): 48 | ''' Takes a sequence and splits it into train - test groups based on 49 | the fractional train_size (0.8 means the train set is the first 80% of 50 | the data). The seq_len is the desired number of datapoints in the 51 | analysis window. 52 | ''' 53 | seq_matrix = [] 54 | sequence_length = seq_len + 1 # for now the target incl. in X as last value 55 | for index in range(len(sequence) - sequence_length): 56 | seq_matrix.append(sequence[index: index + sequence_length]) 57 | seq_matrix = np.array(seq_matrix) 58 | last_row_train = int(round(train_size * seq_matrix.shape[0])) 59 | train = seq_matrix[:last_row_train, :] # everything up to last_row_train is train 60 | np.random.shuffle(train) # shuffle these rows (but each row seq. preserved) 61 | X_train = train[:, :-1] # the last pt in each row not included in X_train 62 | y_train = train[:, -1] # the target was the last pt in each X_train row 63 | X_test = seq_matrix[last_row_train:, :-1] # same for test 64 | y_test = seq_matrix[last_row_train:, -1] 65 | 66 | return X_train, y_train, X_test, y_test 67 | 68 | # inputs used to make the data 69 | cycles = 50 # total number of sin wave cycles 70 | pts_per_cycle = 50 # number of points per cycle 71 | seq_len = 8 # number of datapoints in the training window 72 | train_size = 0.8 # fraction of data set (from beginning) used to train 73 | 74 | ## simulation parameters 75 | #np.random.seed(1) 76 | #alpha = 0.1 # learning rate 77 | #num_epochs = 200 # number of epochs 78 | # 79 | ## make network geometry 80 | #nodes_input = FILL_THIS_IN # this is the sequence training window 81 | #nodes_hidden = 8 82 | #nodes_target = 1 83 | 84 | ## initialize weights 85 | #Wxh = 2*np.random.uniform(size=(nodes_input, nodes_hidden)) - 1 86 | #Whh = FILL_THIS_IN 87 | #Why = 2*np.random.uniform(size=(nodes_hidden, nodes_target)) - 1 88 | 89 | # make the data and visualize it 90 | x, sinewave = make_sine_wave(cycles, pts_per_cycle) 91 | plot_train_test(x, sinewave, train_size) # to check 92 | # train - test split below 93 | X_train, y_train, X_test, y_test = make_train_test_seqs(sinewave, seq_len, train_size) 94 | 95 | 96 | ## training 97 | #print("\nTraining:") 98 | #H_prev = np.zeros((1, nodes_hidden)) 99 | #H_delta_fut = np.zeros(nodes_hidden) 100 | #for e in range(num_epochs): 101 | # error_lst = [] # differences between target and predictions 102 | # for X, y in zip(X_train, y_train): 103 | # X = X.reshape((1, X.shape[0])) # for row, column shape 104 | # y = y.reshape((1,1)) 105 | # # Feed forward 106 | # H = activation(np.dot(X,Wxh) + FILL_THIS_IN ) 107 | # yp = activation(np.dot(H,Why)) 108 | # # Back propogate to find gradients 109 | # # Why gradients 110 | # yp_error = y - yp 111 | # yp_delta = yp_error*activation(yp,deriv=True) 112 | # grad_Why = np.dot(H.T, yp_delta) 113 | # # Wxh gradients 114 | # H_error = np.dot(yp_delta, Why.T) + FILL_THIS_IN 115 | # H_delta = H_error * activation(H,deriv=True) 116 | # #H_delta_fut = np.copy(H_delta) crashes simulation 117 | # grad_Wxh = np.dot(X.T, H_delta) 118 | # # Whh gradients 119 | # grad_Whh = FILL_THIS_IN 120 | # # Use gradient descent to update weights 121 | # Why += alpha * grad_Why 122 | # Whh += FILL_THIS_IN 123 | # Wxh += alpha * grad_Wxh 124 | # # save for future use 125 | # H_prev = np.copy(H) 126 | # error_lst.append(np.abs(yp_error[0][0])) 127 | # 128 | # epoch_error = np.mean(error_lst).round(4) 129 | # if (e % int(num_epochs/20)) == 0: 130 | # print("Epoch: {0:<8s} Error: {1}".format(str(e), epoch_error)) 131 | # 132 | #print("Simulation finished.") 133 | #print("Testing.") 134 | # 135 | ## Test on test set - just predict 1 ahead 136 | #yp_lst = [] # predictions 137 | #H_prev = np.zeros((1, nodes_hidden)) 138 | #for X in X_test: 139 | # X = X.reshape((1, X.shape[0])) # for row, column shape 140 | # # Feed forward 141 | # H = activation(np.dot(X,Wxh) + np.dot(H_prev,Whh)) 142 | # yp = activation(np.dot(H,Why)) 143 | # yp_lst.append(yp[0][0]) 144 | # H_prev = np.copy(H) 145 | # 146 | #plot_predict_vs_test(yp_lst, y_test, 'Test - only plotting 1 ahead') 147 | # 148 | ## Test on test set - after initial seed predict all the rest 149 | #yp_lst = [] # predictions 150 | #H_prev = np.zeros((1, nodes_hidden)) 151 | #for i in range(X_test.shape[0]): 152 | # if i == 1: 153 | # X = X_test[0] 154 | # X = X.reshape((1, X.shape[0])) 155 | # # Feed forward 156 | # H = activation(np.dot(X,Wxh) + np.dot(H_prev,Whh)) 157 | # yp = activation(np.dot(H,Why)) 158 | # yp_lst.append(yp[0][0]) 159 | # H_prev = np.copy(H) 160 | # X = np.append(X[:,1:], yp[0]).reshape((1, nodes_input)) 161 | # 162 | #plot_predict_vs_test(yp_lst, y_test, 'Test - predicting entire test after seed') 163 | 164 | -------------------------------------------------------------------------------- /rnn_soln.py: -------------------------------------------------------------------------------- 1 | #Vanilla RNN predicts sine wave 2 | #based on iamtrask's blog: 3 | #http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/ 4 | #and code in jakob aungiers github: 5 | #https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction 6 | 7 | import numpy as np 8 | import matplotlib.pyplot as plt 9 | 10 | #plotting 11 | def plot_train_test(x, y, train_size): 12 | ''' for visualizing train-test split ''' 13 | split = int(len(x)*train_size) 14 | x_train, y_train = x[:split], y[:split] 15 | x_test, y_test = x[split:], y[split:] 16 | plt.plot(x_train, y_train, 'b-', label='train') 17 | plt.plot(x_test, y_test, 'r-', label='test') 18 | plt.legend() 19 | plt.show() 20 | 21 | def plot_predict_vs_test(y_pred, y_test, figtitle): 22 | ''' visualize peformance on test set ''' 23 | dps = np.arange(0,y_test.shape[0]) 24 | plt.plot(dps, y_test, '-b', label='test') 25 | plt.plot(dps, y_pred, '-g', label='prediction') 26 | plt.legend() 27 | plt.title(figtitle) 28 | plt.show() 29 | 30 | # activation function 31 | def activation(x,deriv=False): 32 | '''activation function, values for tanh''' 33 | if(deriv==True): 34 | return 1 - np.tanh(x)**2 35 | return np.tanh(x) 36 | 37 | # inputs 38 | def make_sine_wave(cycles, pts_per_cycle): 39 | ''' makes a sine wave, centered on 0, with peak-to-peak amplitude 40 | of 2 (-1 to 1), with the desired number of cycles and the number of 41 | points per cycle 42 | ''' 43 | x = np.linspace(0, cycles*2*np.pi, num=cycles*pts_per_cycle+1, endpoint=True) 44 | return x, np.sin(x) 45 | 46 | 47 | def make_train_test_seqs(sequence, seq_len, train_size): 48 | ''' Takes a sequence and splits it into train - test groups based on 49 | the fractional train_size (0.8 means the train set is the first 80% of 50 | the data). The seq_len is the desired number of datapoints in the 51 | analysis window. 52 | ''' 53 | seq_matrix = [] 54 | sequence_length = seq_len + 1 # for now the target incl. in X as last value 55 | for index in range(len(sequence) - sequence_length): 56 | seq_matrix.append(sequence[index: index + sequence_length]) 57 | seq_matrix = np.array(seq_matrix) 58 | last_row_train = int(round(train_size * seq_matrix.shape[0])) 59 | train = seq_matrix[:last_row_train, :] # everything up to last_row_train is train 60 | np.random.shuffle(train) # shuffle these rows (but each row seq. preserved) 61 | X_train = train[:, :-1] # the last pt in each row not included in X_train 62 | y_train = train[:, -1] # the target was the last pt in each X_train row 63 | X_test = seq_matrix[last_row_train:, :-1] # same for test 64 | y_test = seq_matrix[last_row_train:, -1] 65 | 66 | return X_train, y_train, X_test, y_test 67 | 68 | # inputs used to make the data 69 | cycles = 50 # total number of sin wave cycles 70 | pts_per_cycle = 50 # number of points per cycle 71 | seq_len = 8 # number of datapoint in the training window 72 | train_size = 0.8 # fraction of data set (from beginning) used to train 73 | 74 | # simulation parameters 75 | np.random.seed(1) 76 | alpha = 0.1 # learning rate 77 | num_epochs = 1000 # number of epochs 78 | 79 | # make network geometry 80 | nodes_input = seq_len # this is also the sequence training window 81 | nodes_hidden = 16 82 | nodes_target = 1 83 | 84 | # initialize weights 85 | Wxh = 2*np.random.uniform(size=(nodes_input, nodes_hidden)) - 1 86 | Whh = 2*np.random.uniform(size=(nodes_hidden, nodes_hidden)) - 1 87 | Why = 2*np.random.uniform(size=(nodes_hidden, nodes_target)) - 1 88 | 89 | # make the data and visualize it 90 | x, sinewave = make_sine_wave(cycles, pts_per_cycle) 91 | plot_train_test(x, sinewave, train_size) # to check 92 | # train - test split below 93 | X_train, y_train, X_test, y_test = make_train_test_seqs(sinewave, seq_len, train_size) 94 | 95 | 96 | # training 97 | print("\nTraining:") 98 | H_prev = np.zeros((1, nodes_hidden)) 99 | H_delta_fut = np.zeros(nodes_hidden) 100 | for e in range(num_epochs): 101 | error_lst = [] # differences between target and predictions 102 | for X, y in zip(X_train, y_train): 103 | X = X.reshape((1, X.shape[0])) # for row, column shape 104 | y = y.reshape((1,1)) 105 | # Feed forward 106 | H = activation(np.dot(X,Wxh) + np.dot(H_prev,Whh)) 107 | yp = activation(np.dot(H,Why)) 108 | # Back propogate to find gradients 109 | # Why gradients 110 | yp_error = y - yp 111 | yp_delta = yp_error*activation(yp,deriv=True) 112 | grad_Why = np.dot(H.T, yp_delta) 113 | # Wxh gradients 114 | H_error = np.dot(yp_delta, Why.T) + np.dot(H_delta_fut, Whh.T) 115 | H_delta = H_error * activation(H,deriv=True) 116 | #H_delta_fut = np.copy(H_delta) crashes simulation 117 | grad_Wxh = np.dot(X.T, H_delta) 118 | # Whh gradients 119 | grad_Whh = np.dot(H_prev.T, H_delta) 120 | # Use gradient descent to update weights 121 | Why += alpha * grad_Why 122 | Whh += alpha * grad_Whh 123 | Wxh += alpha * grad_Wxh 124 | # save for future use 125 | H_prev = np.copy(H) 126 | error_lst.append(np.abs(yp_error[0][0])) 127 | 128 | epoch_error = np.mean(error_lst).round(4) 129 | if (e % int(num_epochs/20)) == 0: 130 | print("Epoch: {0:<8s} Error: {1}".format(str(e), epoch_error)) 131 | 132 | print("Simulation finished.") 133 | print("Testing.") 134 | 135 | # Test on test set - just predict 1 ahead 136 | yp_lst = [] # predictions 137 | H_prev = np.zeros((1, nodes_hidden)) 138 | for X in X_test: 139 | X = X.reshape((1, X.shape[0])) # for row, column shape 140 | # Feed forward 141 | H = activation(np.dot(X,Wxh) + np.dot(H_prev,Whh)) 142 | yp = activation(np.dot(H,Why)) 143 | yp_lst.append(yp[0][0]) 144 | H_prev = np.copy(H) 145 | 146 | plot_predict_vs_test(yp_lst, y_test, 'Test - only plotting 1 ahead') 147 | 148 | # Test on test set - after initial seed predict all the rest 149 | yp_lst = [] # predictions 150 | H_prev = np.zeros((1, nodes_hidden)) 151 | for i in range(X_test.shape[0]): 152 | if i == 1: 153 | X = X_test[0] 154 | X = X.reshape((1, X.shape[0])) 155 | # Feed forward 156 | H = activation(np.dot(X,Wxh) + np.dot(H_prev,Whh)) 157 | yp = activation(np.dot(H,Why)) 158 | yp_lst.append(yp[0][0]) 159 | H_prev = np.copy(H) 160 | X = np.append(X[:,1:], yp[0]).reshape((1, nodes_input)) 161 | 162 | plot_predict_vs_test(yp_lst, y_test, 'Test - predicting entire test after seed') 163 | 164 | -------------------------------------------------------------------------------- /run_lstm.py: -------------------------------------------------------------------------------- 1 | # LSTM example for sine wave and stocks from blog: 2 | # http://www.jakob-aungiers.com/articles/a/LSTM-Neural-Network-for-Time-Series-Prediction 3 | # base code from: 4 | # https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction 5 | 6 | import lstm 7 | import time 8 | import matplotlib.pyplot as plt 9 | import numpy as np 10 | 11 | def plot_results(predicted_data, true_data, figtitle): 12 | ''' use when predicting just one analysis window ''' 13 | fig = plt.figure(facecolor='white') 14 | ax = fig.add_subplot(111) 15 | ax.plot(true_data, label='True Data') 16 | plt.plot(predicted_data, label='Prediction') 17 | plt.legend() 18 | plt.title(figtitle) 19 | plt.show() 20 | 21 | def plot_results_multiple(predicted_data, true_data, prediction_len): 22 | ''' use when predicting multiple analyses windows in data ''' 23 | fig = plt.figure(facecolor='white') 24 | ax = fig.add_subplot(111) 25 | ax.plot(true_data, label='True Data') 26 | #Pad the list of predictions to shift it in the graph to it's correct start 27 | for i, data in enumerate(predicted_data): 28 | if i != 0: 29 | padding = [None for p in range(i * prediction_len)] 30 | plt.plot(padding + data, label='Prediction') 31 | plt.legend() 32 | plt.show() 33 | 34 | #Main Run Thread 35 | if __name__=='__main__': 36 | global_start_time = time.time() 37 | epochs = 100 # suggest 100 for sine wave, 10 for stock 38 | seq_len = 25 # suggest using 25 for sine wave, 50 for stock 39 | 40 | print('> Loading data... ') 41 | 42 | # choose either the sine wave data or stock data 43 | X_train, y_train, X_test, y_test = lstm.load_data('sinwave.csv', seq_len, False) # data is sine wave 44 | #X_train, y_train, X_test, y_test = lstm.load_data('sp500.csv', seq_len, True) # data is a stock, normalize data is True 45 | 46 | print('> Data Loaded. Compiling...') 47 | 48 | model = lstm.build_model([1, seq_len, 100, 1]) # 1 input layer, layer 1 has seq_len neurons, layer 2 has 100 neurons, 1 output 49 | 50 | model.fit( 51 | X_train, 52 | y_train, 53 | batch_size=512, 54 | nb_epoch=epochs, 55 | validation_split=0.05) 56 | 57 | print('> Completed.') 58 | print('Training duration (s) : ', time.time() - global_start_time) 59 | 60 | # comment out either sine wave prediction code or stock prediction code 61 | # sine wave code 62 | predicted = lstm.predict_point_by_point(model, X_test) 63 | plot_results(predicted, y_test, 'Sine wave - predict one point ahead') 64 | predicted_full = lstm.predict_sequence_full(model, X_test, seq_len) 65 | plot_results(predicted_full, y_test, 'Sine wave - predict full sequence from start seed') 66 | 67 | # stock prediction code 68 | #predictions = lstm.predict_sequences_multiple(model, X_test, seq_len, seq_len) #model, data, window_size, prediction length) 69 | #plot_results_multiple(predictions, y_test, seq_len) # prediction, true data, prediction length) 70 | -------------------------------------------------------------------------------- /sinwave.csv: -------------------------------------------------------------------------------- 1 | 0.841470985 2 | 0.873736397 3 | 0.90255357 4 | 0.927808777 5 | 0.949402346 6 | 0.967249058 7 | 0.98127848 8 | 0.991435244 9 | 0.997679266 10 | 0.999985904 11 | 0.998346054 12 | 0.992766189 13 | 0.983268329 14 | 0.969889958 15 | 0.952683874 16 | 0.931717983 17 | 0.907075026 18 | 0.878852258 19 | 0.847161063 20 | 0.812126509 21 | 0.773886863 22 | 0.73259304 23 | 0.688408006 24 | 0.64150614 25 | 0.592072543 26 | 0.540302306 27 | 0.486399742 28 | 0.430577581 29 | 0.373056127 30 | 0.314062391 31 | 0.253829194 32 | 0.192594249 33 | 0.130599223 34 | 0.068088781 35 | 0.005309624 36 | -0.057490488 37 | -0.120063711 38 | -0.182163097 39 | -0.243543569 40 | -0.303962886 41 | -0.3631826 42 | -0.420968998 43 | -0.477094024 44 | -0.531336178 45 | -0.583481391 46 | 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1013 | 1130.52002 1014 | 1132.050049 1015 | 1139.829956 1016 | 1138.77002 1017 | 1147.619995 1018 | 1143.939941 1019 | 1141.550049 1020 | 1155.369995 1021 | 1144.050049 1022 | 1128.47998 1023 | 1134.109985 1024 | 1131.130005 1025 | 1135.26001 1026 | 1136.030029 1027 | 1126.52002 1028 | 1128.589966 1029 | 1142.76001 1030 | 1139.810059 1031 | 1145.540039 1032 | 1157.76001 1033 | 1152.109985 1034 | 1145.810059 1035 | 1156.98999 1036 | 1151.819946 1037 | 1147.060059 1038 | 1144.109985 1039 | 1140.98999 1040 | 1139.089966 1041 | 1143.670044 1042 | 1144.910034 1043 | 1144.939941 1044 | 1155.969971 1045 | 1149.099976 1046 | 1151.030029 1047 | 1154.869995 1048 | 1156.859985 1049 | 1147.199951 1050 | 1140.579956 1051 | 1123.890015 1052 | 1106.780029 1053 | 1120.569946 1054 | 1104.48999 1055 | 1110.699951 1056 | 1123.75 1057 | 1122.319946 1058 | 1109.780029 1059 | 1095.400024 1060 | 1093.949951 1061 | 1091.329956 1062 | 1109.189941 1063 | 1108.060059 1064 | 1122.469971 1065 | 1127 1066 | 1126.209961 1067 | 1132.170044 1068 | 1141.810059 1069 | 1150.569946 1070 | 1148.160034 1071 | 1140.530029 1072 | 1139.319946 1073 | 1145.199951 1074 | 1129.439941 1075 | 1128.170044 1076 | 1128.839966 1077 | 1134.609985 1078 | 1135.819946 1079 | 1118.150024 1080 | 1124.089966 1081 | 1139.930054 1082 | 1140.599976 1083 | 1135.530029 1084 | 1138.109985 1085 | 1122.410034 1086 | 1113.890015 1087 | 1107.300049 1088 | 1117.48999 1089 | 1119.550049 1090 | 1121.530029 1091 | 1113.98999 1092 | 1098.699951 1093 | 1087.119995 1094 | 1095.449951 1095 | 1097.280029 1096 | 1096.439941 1097 | 1095.699951 1098 | 1084.099976 1099 | 1091.48999 1100 | 1088.680054 1101 | 1089.189941 1102 | 1093.560059 1103 | 1095.410034 1104 | 1113.050049 1105 | 1114.939941 1106 | 1121.280029 1107 | 1120.680054 1108 | 1121.199951 1109 | 1124.98999 1110 | 1116.640015 1111 | 1122.5 1112 | 1140.420044 1113 | 1142.180054 1114 | 1131.329956 1115 | 1136.469971 1116 | 1125.290039 1117 | 1132.01001 1118 | 1133.560059 1119 | 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| 1105.910034 1173 | 1118.310059 1174 | 1113.630005 1175 | 1121.300049 1176 | 1116.27002 1177 | 1118.380005 1178 | 1123.920044 1179 | 1125.819946 1180 | 1128.329956 1181 | 1120.369995 1182 | 1123.5 1183 | 1128.550049 1184 | 1122.199951 1185 | 1129.300049 1186 | 1113.560059 1187 | 1108.359985 1188 | 1110.109985 1189 | 1103.52002 1190 | 1110.060059 1191 | 1114.800049 1192 | 1114.579956 1193 | 1131.5 1194 | 1135.170044 1195 | 1134.47998 1196 | 1142.050049 1197 | 1130.650024 1198 | 1122.140015 1199 | 1124.390015 1200 | 1121.839966 1201 | 1113.650024 1202 | 1103.290039 1203 | 1108.199951 1204 | 1114.02002 1205 | 1103.22998 1206 | 1103.660034 1207 | 1106.48999 1208 | 1095.73999 1209 | 1094.800049 1210 | 1111.089966 1211 | 1125.400024 1212 | 1127.439941 1213 | 1130.199951 1214 | 1130.51001 1215 | 1130.560059 1216 | 1143.199951 1217 | 1161.670044 1218 | 1166.170044 1219 | 1164.890015 1220 | 1164.079956 1221 | 1162.910034 1222 | 1173.47998 1223 | 1184.170044 1224 | 1183.810059 1225 | 1175.430054 1226 | 1181.939941 1227 | 1183.550049 1228 | 1170.339966 1229 | 1177.23999 1230 | 1176.939941 1231 | 1181.76001 1232 | 1182.650024 1233 | 1178.569946 1234 | 1173.819946 1235 | 1191.369995 1236 | 1190.329956 1237 | 1191.170044 1238 | 1190.25 1239 | 1177.069946 1240 | 1182.810059 1241 | 1189.23999 1242 | 1188 1243 | 1198.680054 1244 | 1203.380005 1245 | 1205.719971 1246 | 1203.209961 1247 | 1194.199951 1248 | 1194.650024 1249 | 1205.449951 1250 | 1209.569946 1251 | 1210.130005 1252 | 1204.920044 1253 | 1213.540039 1254 | 1213.449951 1255 | 1213.550049 1256 | 1211.920044 1257 | 1202.079956 1258 | 1188.050049 1259 | 1183.73999 1260 | 1187.890015 1261 | 1186.189941 1262 | 1190.25 1263 | 1182.98999 1264 | 1187.699951 1265 | 1177.449951 1266 | 1184.52002 1267 | 1195.97998 1268 | 1184.630005 1269 | 1175.410034 1270 | 1167.869995 1271 | 1163.75 1272 | 1168.410034 1273 | 1174.069946 1274 | 1174.550049 1275 | 1171.359985 1276 | 1181.27002 1277 | 1189.410034 1278 | 1193.189941 1279 | 1189.890015 1280 | 1203.030029 1281 | 1201.719971 1282 | 1202.300049 1283 | 1191.98999 1284 | 1197.01001 1285 | 1205.300049 1286 | 1206.140015 1287 | 1210.119995 1288 | 1210.339966 1289 | 1200.75 1290 | 1201.589966 1291 | 1184.160034 1292 | 1190.800049 1293 | 1200.199951 1294 | 1211.369995 1295 | 1203.599976 1296 | 1210.410034 1297 | 1210.079956 1298 | 1210.469971 1299 | 1222.119995 1300 | 1225.310059 1301 | 1219.430054 1302 | 1207.01001 1303 | 1209.25 1304 | 1200.079956 1305 | 1206.829956 1306 | 1197.75 1307 | 1188.069946 1308 | 1190.209961 1309 | 1189.650024 1310 | 1183.780029 1311 | 1171.709961 1312 | 1172.530029 1313 | 1171.420044 1314 | 1174.280029 1315 | 1165.359985 1316 | 1181.410034 1317 | 1180.589966 1318 | 1172.920044 1319 | 1176.119995 1320 | 1181.390015 1321 | 1184.069946 1322 | 1191.140015 1323 | 1181.199951 1324 | 1181.209961 1325 | 1187.76001 1326 | 1173.790039 1327 | 1162.050049 1328 | 1142.619995 1329 | 1145.97998 1330 | 1152.780029 1331 | 1137.5 1332 | 1159.949951 1333 | 1152.119995 1334 | 1162.099976 1335 | 1151.829956 1336 | 1156.380005 1337 | 1143.219971 1338 | 1156.849976 1339 | 1162.160034 1340 | 1161.170044 1341 | 1175.650024 1342 | 1172.630005 1343 | 1171.349976 1344 | 1178.839966 1345 | 1166.219971 1346 | 1171.109985 1347 | 1159.359985 1348 | 1154.050049 1349 | 1165.689941 1350 | 1173.800049 1351 | 1185.560059 1352 | 1191.079956 1353 | 1189.280029 1354 | 1193.859985 1355 | 1194.069946 1356 | 1190.01001 1357 | 1197.619995 1358 | 1198.780029 1359 | 1191.5 1360 | 1202.219971 1361 | 1204.290039 1362 | 1196.02002 1363 | 1197.51001 1364 | 1197.26001 1365 | 1194.670044 1366 | 1200.930054 1367 | 1198.109985 1368 | 1200.819946 1369 | 1203.910034 1370 | 1206.579956 1371 | 1210.959961 1372 | 1216.959961 1373 | 1216.099976 1374 | 1213.609985 1375 | 1213.880005 1376 | 1200.72998 1377 | 1191.569946 1378 | 1190.689941 1379 | 1201.569946 1380 | 1199.849976 1381 | 1191.329956 1382 | 1194.439941 1383 | 1204.98999 1384 | 1194.939941 1385 | 1197.869995 1386 | 1211.859985 1387 | 1219.439941 1388 | 1222.209961 1389 | 1223.290039 1390 | 1226.5 1391 | 1227.920044 1392 | 1221.130005 1393 | 1229.349976 1394 | 1235.199951 1395 | 1227.040039 1396 | 1233.680054 1397 | 1229.030029 1398 | 1231.160034 1399 | 1236.790039 1400 | 1243.719971 1401 | 1234.180054 1402 | 1235.349976 1403 | 1244.119995 1404 | 1245.040039 1405 | 1235.859985 1406 | 1226.420044 1407 | 1223.130005 1408 | 1231.380005 1409 | 1229.130005 1410 | 1237.810059 1411 | 1230.390015 1412 | 1233.869995 1413 | 1219.339966 1414 | 1220.23999 1415 | 1219.02002 1416 | 1219.709961 1417 | 1221.72998 1418 | 1217.589966 1419 | 1209.589966 1420 | 1212.369995 1421 | 1205.099976 1422 | 1212.280029 1423 | 1208.410034 1424 | 1220.329956 1425 | 1221.589966 1426 | 1218.02002 1427 | 1233.390015 1428 | 1236.359985 1429 | 1231.670044 1430 | 1241.47998 1431 | 1240.560059 1432 | 1231.199951 1433 | 1227.160034 1434 | 1227.72998 1435 | 1237.910034 1436 | 1231.02002 1437 | 1221.339966 1438 | 1210.199951 1439 | 1214.619995 1440 | 1215.290039 1441 | 1215.630005 1442 | 1215.660034 1443 | 1216.890015 1444 | 1227.680054 1445 | 1228.810059 1446 | 1226.699951 1447 | 1214.469971 1448 | 1196.390015 1449 | 1191.48999 1450 | 1195.900024 1451 | 1187.329956 1452 | 1184.869995 1453 | 1177.680054 1454 | 1176.839966 1455 | 1186.569946 1456 | 1190.099976 1457 | 1178.140015 1458 | 1195.76001 1459 | 1177.800049 1460 | 1179.589966 1461 | 1199.380005 1462 | 1196.540039 1463 | 1191.380005 1464 | 1178.900024 1465 | 1198.410034 1466 | 1207.01001 1467 | 1202.76001 1468 | 1214.76001 1469 | 1219.939941 1470 | 1220.140015 1471 | 1222.810059 1472 | 1218.589966 1473 | 1220.650024 1474 | 1230.959961 1475 | 1234.719971 1476 | 1233.76001 1477 | 1229.01001 1478 | 1231.209961 1479 | 1242.800049 1480 | 1248.27002 1481 | 1254.849976 1482 | 1261.22998 1483 | 1265.609985 1484 | 1268.25 1485 | 1257.459961 1486 | 1257.47998 1487 | 1249.47998 1488 | 1264.670044 1489 | 1265.079956 1490 | 1262.089966 1491 | 1263.699951 1492 | 1257.369995 1493 | 1255.839966 1494 | 1259.369995 1495 | 1260.430054 1496 | 1267.430054 1497 | 1272.73999 1498 | 1270.939941 1499 | 1267.319946 1500 | 1259.920044 1501 | 1259.619995 1502 | 1262.790039 1503 | 1268.119995 1504 | 1268.660034 1505 | 1256.540039 1506 | 1258.170044 1507 | 1254.420044 1508 | 1248.290039 1509 | 1268.800049 1510 | 1273.459961 1511 | 1273.47998 1512 | 1285.449951 1513 | 1290.150024 1514 | 1289.689941 1515 | 1294.180054 1516 | 1286.060059 1517 | 1287.609985 1518 | 1282.930054 1519 | 1277.930054 1520 | 1285.040039 1521 | 1261.48999 1522 | 1263.819946 1523 | 1266.859985 1524 | 1264.680054 1525 | 1273.829956 1526 | 1283.719971 1527 | 1285.189941 1528 | 1280.079956 1529 | 1282.459961 1530 | 1270.839966 1531 | 1264.030029 1532 | 1265.02002 1533 | 1254.780029 1534 | 1265.650024 1535 | 1263.780029 1536 | 1266.98999 1537 | 1262.859985 1538 | 1275.530029 1539 | 1280 1540 | 1289.380005 1541 | 1287.23999 1542 | 1283.030029 1543 | 1292.670044 1544 | 1287.790039 1545 | 1289.430054 1546 | 1294.119995 1547 | 1280.660034 1548 | 1291.23999 1549 | 1289.140015 1550 | 1287.22998 1551 | 1278.26001 1552 | 1275.880005 1553 | 1278.469971 1554 | 1272.22998 1555 | 1281.420044 1556 | 1284.130005 1557 | 1297.47998 1558 | 1303.02002 1559 | 1305.329956 1560 | 1307.25 1561 | 1305.079956 1562 | 1297.22998 1563 | 1305.040039 1564 | 1301.670044 1565 | 1302.949951 1566 | 1301.609985 1567 | 1293.22998 1568 | 1302.890015 1569 | 1300.25 1570 | 1294.869995 1571 | 1297.810059 1572 | 1305.930054 1573 | 1311.560059 1574 | 1309.040039 1575 | 1295.5 1576 | 1296.619995 1577 | 1286.569946 1578 | 1288.119995 1579 | 1289.119995 1580 | 1285.329956 1581 | 1307.280029 1582 | 1309.930054 1583 | 1311.459961 1584 | 1311.280029 1585 | 1308.109985 1586 | 1301.73999 1587 | 1305.410034 1588 | 1309.719971 1589 | 1310.609985 1590 | 1305.189941 1591 | 1313.209961 1592 | 1308.119995 1593 | 1312.25 1594 | 1325.76001 1595 | 1324.660034 1596 | 1325.140015 1597 | 1322.849976 1598 | 1305.920044 1599 | 1291.23999 1600 | 1294.5 1601 | 1292.079956 1602 | 1270.319946 1603 | 1261.810059 1604 | 1267.030029 1605 | 1262.069946 1606 | 1256.579956 1607 | 1258.569946 1608 | 1272.880005 1609 | 1280.160034 1610 | 1259.869995 1611 | 1270.089966 1612 | 1285.709961 1613 | 1288.219971 1614 | 1265.290039 1615 | 1263.849976 1616 | 1256.150024 1617 | 1257.930054 1618 | 1252.300049 1619 | 1237.439941 1620 | 1223.689941 1621 | 1230.040039 1622 | 1256.160034 1623 | 1251.540039 1624 | 1240.130005 1625 | 1240.119995 1626 | 1252.199951 1627 | 1245.599976 1628 | 1244.5 1629 | 1250.560059 1630 | 1239.199951 1631 | 1246 1632 | 1272.869995 1633 | 1270.199951 1634 | 1280.189941 1635 | 1270.910034 1636 | 1274.079956 1637 | 1265.47998 1638 | 1267.339966 1639 | 1272.430054 1640 | 1258.599976 1641 | 1242.280029 1642 | 1236.199951 1643 | 1234.48999 1644 | 1236.859985 1645 | 1259.810059 1646 | 1249.130005 1647 | 1240.290039 1648 | 1260.910034 1649 | 1268.880005 1650 | 1268.400024 1651 | 1263.199951 1652 | 1278.550049 1653 | 1276.660034 1654 | 1270.920044 1655 | 1277.410034 1656 | 1280.27002 1657 | 1279.359985 1658 | 1275.77002 1659 | 1271.47998 1660 | 1265.949951 1661 | 1271.810059 1662 | 1266.73999 1663 | 1268.209961 1664 | 1285.579956 1665 | 1295.430054 1666 | 1297.47998 1667 | 1302.300049 1668 | 1297.52002 1669 | 1298.819946 1670 | 1292.98999 1671 | 1296.060059 1672 | 1295.089966 1673 | 1301.780029 1674 | 1304.280029 1675 | 1305.369995 1676 | 1303.819946 1677 | 1311.01001 1678 | 1313.25 1679 | 1300.26001 1680 | 1294.02002 1681 | 1298.920044 1682 | 1299.540039 1683 | 1313 1684 | 1318.069946 1685 | 1316.280029 1686 | 1319.660034 1687 | 1321.180054 1688 | 1317.640015 1689 | 1325.180054 1690 | 1318.030029 1691 | 1314.780029 1692 | 1326.369995 1693 | 1336.349976 1694 | 1336.589966 1695 | 1338.880005 1696 | 1335.849976 1697 | 1331.319946 1698 | 1334.109985 1699 | 1350.199951 1700 | 1353.219971 1701 | 1349.589966 1702 | 1350.660034 1703 | 1353.420044 1704 | 1349.949951 1705 | 1362.829956 1706 | 1365.619995 1707 | 1369.060059 1708 | 1364.050049 1709 | 1365.800049 1710 | 1366.959961 1711 | 1368.599976 1712 | 1377.02002 1713 | 1377.380005 1714 | 1382.219971 1715 | 1389.079956 1716 | 1377.339966 1717 | 1377.930054 1718 | 1377.939941 1719 | 1367.810059 1720 | 1367.339966 1721 | 1364.300049 1722 | 1379.780029 1723 | 1382.839966 1724 | 1385.719971 1725 | 1378.329956 1726 | 1380.900024 1727 | 1384.420044 1728 | 1393.219971 1729 | 1396.569946 1730 | 1399.76001 1731 | 1401.199951 1732 | 1400.5 1733 | 1402.810059 1734 | 1406.089966 1735 | 1400.949951 1736 | 1381.959961 1737 | 1386.719971 1738 | 1399.47998 1739 | 1400.630005 1740 | 1396.709961 1741 | 1409.119995 1742 | 1414.76001 1743 | 1412.900024 1744 | 1407.290039 1745 | 1409.839966 1746 | 1413.040039 1747 | 1411.560059 1748 | 1413.209961 1749 | 1425.48999 1750 | 1427.089966 1751 | 1422.47998 1752 | 1425.550049 1753 | 1423.530029 1754 | 1418.300049 1755 | 1410.76001 1756 | 1416.900024 1757 | 1426.839966 1758 | 1424.72998 1759 | 1418.300049 1760 | 1416.599976 1761 | 1418.339966 1762 | 1409.709961 1763 | 1412.839966 1764 | 1412.109985 1765 | 1414.849976 1766 | 1423.819946 1767 | 1430.72998 1768 | 1431.900024 1769 | 1430.619995 1770 | 1426.369995 1771 | 1430.5 1772 | 1422.949951 1773 | 1427.98999 1774 | 1440.130005 1775 | 1423.900024 1776 | 1422.180054 1777 | 1420.619995 1778 | 1428.819946 1779 | 1438.23999 1780 | 1445.939941 1781 | 1448.390015 1782 | 1446.98999 1783 | 1448 1784 | 1450.02002 1785 | 1448.310059 1786 | 1438.060059 1787 | 1433.369995 1788 | 1444.26001 1789 | 1455.300049 1790 | 1456.810059 1791 | 1455.540039 1792 | 1459.680054 1793 | 1457.630005 1794 | 1456.380005 1795 | 1451.189941 1796 | 1449.369995 1797 | 1399.040039 1798 | 1406.819946 1799 | 1403.170044 1800 | 1387.170044 1801 | 1374.119995 1802 | 1395.410034 1803 | 1391.969971 1804 | 1401.890015 1805 | 1402.839966 1806 | 1406.599976 1807 | 1377.949951 1808 | 1387.170044 1809 | 1392.280029 1810 | 1386.949951 1811 | 1402.060059 1812 | 1410.939941 1813 | 1435.040039 1814 | 1434.540039 1815 | 1436.109985 1816 | 1437.5 1817 | 1428.609985 1818 | 1417.22998 1819 | 1422.530029 1820 | 1420.859985 1821 | 1424.550049 1822 | 1437.77002 1823 | 1439.369995 1824 | 1443.76001 1825 | 1444.609985 1826 | 1448.390015 1827 | 1438.869995 1828 | 1447.800049 1829 | 1452.849976 1830 | 1468.329956 1831 | 1471.47998 1832 | 1472.5 1833 | 1470.72998 1834 | 1484.349976 1835 | 1480.930054 1836 | 1480.410034 1837 | 1495.420044 1838 | 1494.25 1839 | 1494.069946 1840 | 1482.369995 1841 | 1486.300049 1842 | 1495.920044 1843 | 1502.390015 1844 | 1505.619995 1845 | 1509.47998 1846 | 1507.719971 1847 | 1512.579956 1848 | 1491.469971 1849 | 1505.849976 1850 | 1503.150024 1851 | 1501.189941 1852 | 1514.140015 1853 | 1512.75 1854 | 1522.75 1855 | 1525.099976 1856 | 1524.119995 1857 | 1522.280029 1858 | 1507.51001 1859 | 1515.72998 1860 | 1518.109985 1861 | 1530.22998 1862 | 1530.619995 1863 | 1536.339966 1864 | 1539.180054 1865 | 1530.949951 1866 | 1517.380005 1867 | 1490.719971 1868 | 1507.670044 1869 | 1509.119995 1870 | 1493 1871 | 1515.670044 1872 | 1522.969971 1873 | 1532.910034 1874 | 1531.050049 1875 | 1533.699951 1876 | 1512.839966 1877 | 1522.189941 1878 | 1502.560059 1879 | 1497.73999 1880 | 1492.890015 1881 | 1506.339966 1882 | 1505.709961 1883 | 1503.349976 1884 | 1519.430054 1885 | 1524.869995 1886 | 1525.400024 1887 | 1530.439941 1888 | 1531.849976 1889 | 1510.119995 1890 | 1518.76001 1891 | 1547.699951 1892 | 1552.5 1893 | 1549.52002 1894 | 1549.369995 1895 | 1546.170044 1896 | 1553.079956 1897 | 1534.099976 1898 | 1541.569946 1899 | 1511.040039 1900 | 1518.089966 1901 | 1482.660034 1902 | 1458.949951 1903 | 1473.910034 1904 | 1455.27002 1905 | 1465.810059 1906 | 1472.199951 1907 | 1433.060059 1908 | 1467.670044 1909 | 1476.709961 1910 | 1497.48999 1911 | 1453.089966 1912 | 1453.640015 1913 | 1452.920044 1914 | 1426.540039 1915 | 1406.699951 1916 | 1411.27002 1917 | 1445.939941 1918 | 1445.550049 1919 | 1447.119995 1920 | 1464.069946 1921 | 1462.5 1922 | 1479.369995 1923 | 1466.790039 1924 | 1432.359985 1925 | 1463.76001 1926 | 1457.640015 1927 | 1473.98999 1928 | 1489.420044 1929 | 1472.290039 1930 | 1478.550049 1931 | 1453.550049 1932 | 1451.699951 1933 | 1471.48999 1934 | 1471.560059 1935 | 1483.949951 1936 | 1484.25 1937 | 1476.650024 1938 | 1519.780029 1939 | 1529.030029 1940 | 1518.75 1941 | 1525.75 1942 | 1517.72998 1943 | 1517.209961 1944 | 1525.420044 1945 | 1531.380005 1946 | 1526.75 1947 | 1547.040039 1948 | 1546.630005 1949 | 1539.589966 1950 | 1542.839966 1951 | 1557.589966 1952 | 1552.579956 1953 | 1565.150024 1954 | 1562.469971 1955 | 1554.410034 1956 | 1561.800049 1957 | 1548.709961 1958 | 1538.530029 1959 | 1541.23999 1960 | 1540.079956 1961 | 1500.630005 1962 | 1506.329956 1963 | 1519.589966 1964 | 1515.880005 1965 | 1514.400024 1966 | 1535.280029 1967 | 1540.97998 1968 | 1531.02002 1969 | 1549.380005 1970 | 1508.439941 1971 | 1509.650024 1972 | 1502.170044 1973 | 1520.27002 1974 | 1475.619995 1975 | 1474.77002 1976 | 1453.699951 1977 | 1439.180054 1978 | 1481.050049 1979 | 1470.579956 1980 | 1451.150024 1981 | 1458.73999 1982 | 1433.27002 1983 | 1439.699951 1984 | 1416.77002 1985 | 1440.699951 1986 | 1407.219971 1987 | 1428.22998 1988 | 1469.02002 1989 | 1469.719971 1990 | 1481.140015 1991 | 1472.420044 1992 | 1462.790039 1993 | 1485.01001 1994 | 1507.339966 1995 | 1504.660034 1996 | 1515.959961 1997 | 1477.650024 1998 | 1486.589966 1999 | 1488.410034 2000 | 1467.949951 2001 | 1445.900024 2002 | 1454.97998 2003 | 1453 2004 | 1460.119995 2005 | 1484.459961 2006 | 1496.449951 2007 | 1497.660034 2008 | 1476.27002 2009 | 1478.48999 2010 | 1468.359985 2011 | 1447.160034 2012 | 1447.160034 2013 | 1411.630005 2014 | 1416.180054 2015 | 1390.189941 2016 | 1409.130005 2017 | 1420.329956 2018 | 1401.02002 2019 | 1416.25 2020 | 1380.949951 2021 | 1373.199951 2022 | 1333.25 2023 | 1325.189941 2024 | 1310.5 2025 | 1338.599976 2026 | 1352.069946 2027 | 1330.609985 2028 | 1353.959961 2029 | 1362.300049 2030 | 1355.810059 2031 | 1378.550049 2032 | 1395.420044 2033 | 1380.819946 2034 | 1336.640015 2035 | 1326.449951 2036 | 1336.910034 2037 | 1331.290039 2038 | 1339.130005 2039 | 1348.859985 2040 | 1367.209961 2041 | 1348.859985 2042 | 1349.98999 2043 | 1348.780029 2044 | 1360.030029 2045 | 1342.530029 2046 | 1353.109985 2047 | 1371.800049 2048 | 1381.290039 2049 | 1380.02002 2050 | 1367.680054 2051 | 1330.630005 2052 | 1331.339966 2053 | 1326.75 2054 | 1333.699951 2055 | 1304.339966 2056 | 1293.369995 2057 | 1273.369995 2058 | 1320.650024 2059 | 1308.77002 2060 | 1315.47998 2061 | 1288.140015 2062 | 1276.599976 2063 | 1330.73999 2064 | 1298.420044 2065 | 1329.51001 2066 | 1349.880005 2067 | 1352.98999 2068 | 1341.130005 2069 | 1325.76001 2070 | 1315.219971 2071 | 1322.699951 2072 | 1370.180054 2073 | 1367.530029 2074 | 1369.310059 2075 | 1370.400024 2076 | 1372.540039 2077 | 1365.540039 2078 | 1354.48999 2079 | 1360.550049 2080 | 1332.829956 2081 | 1328.319946 2082 | 1334.430054 2083 | 1364.709961 2084 | 1365.560059 2085 | 1390.329956 2086 | 1388.170044 2087 | 1375.939941 2088 | 1379.930054 2089 | 1388.819946 2090 | 1397.839966 2091 | 1396.369995 2092 | 1390.939941 2093 | 1385.589966 2094 | 1409.339966 2095 | 1413.900024 2096 | 1407.48999 2097 | 1418.26001 2098 | 1392.569946 2099 | 1397.680054 2100 | 1388.280029 2101 | 1403.579956 2102 | 1403.040039 2103 | 1408.660034 2104 | 1423.569946 2105 | 1425.349976 2106 | 1426.630005 2107 | 1413.400024 2108 | 1390.709961 2109 | 1394.349976 2110 | 1375.930054 2111 | 1385.349976 2112 | 1390.839966 2113 | 1398.26001 2114 | 1400.380005 2115 | 1385.670044 2116 | 1377.650024 2117 | 1377.199951 2118 | 1404.050049 2119 | 1360.680054 2120 | 1361.76001 2121 | 1358.439941 2122 | 1335.48999 2123 | 1339.869995 2124 | 1360.030029 2125 | 1360.140015 2126 | 1350.930054 2127 | 1337.810059 2128 | 1342.829956 2129 | 1317.930054 2130 | 1318 2131 | 1314.290039 2132 | 1321.969971 2133 | 1283.150024 2134 | 1278.380005 2135 | 1280 2136 | 1284.910034 2137 | 1261.52002 2138 | 1262.900024 2139 | 1252.310059 2140 | 1273.699951 2141 | 1244.689941 2142 | 1253.390015 2143 | 1239.48999 2144 | 1228.300049 2145 | 1214.910034 2146 | 1245.359985 2147 | 1260.319946 2148 | 1260.680054 2149 | 1260 2150 | 1277 2151 | 1282.189941 2152 | 1252.540039 2153 | 1257.76001 2154 | 1234.369995 2155 | 1263.199951 2156 | 1284.26001 2157 | 1267.380005 2158 | 1260.310059 2159 | 1249.01001 2160 | 1284.880005 2161 | 1289.189941 2162 | 1266.069946 2163 | 1296.319946 2164 | 1305.319946 2165 | 1289.589966 2166 | 1285.829956 2167 | 1292.930054 2168 | 1298.199951 2169 | 1278.599976 2170 | 1266.689941 2171 | 1274.540039 2172 | 1277.719971 2173 | 1292.199951 2174 | 1266.839966 2175 | 1271.51001 2176 | 1281.660034 2177 | 1300.680054 2178 | 1282.829956 2179 | 1277.579956 2180 | 1274.97998 2181 | 1236.829956 2182 | 1242.310059 2183 | 1267.790039 2184 | 1224.51001 2185 | 1232.040039 2186 | 1249.050049 2187 | 1251.699951 2188 | 1192.699951 2189 | 1213.599976 2190 | 1156.390015 2191 | 1206.51001 2192 | 1255.079956 2193 | 1207.089966 2194 | 1188.219971 2195 | 1185.869995 2196 | 1209.180054 2197 | 1213.27002 2198 | 1106.420044 2199 | 1166.359985 2200 | 1161.060059 2201 | 1114.280029 2202 | 1099.22998 2203 | 1056.890015 2204 | 996.22998 2205 | 984.940002 2206 | 909.919983 2207 | 899.219971 2208 | 1003.349976 2209 | 998.01001 2210 | 907.840027 2211 | 946.429993 2212 | 940.549988 2213 | 985.400024 2214 | 955.049988 2215 | 896.780029 2216 | 908.109985 2217 | 876.77002 2218 | 848.919983 2219 | 940.51001 2220 | 930.090027 2221 | 954.090027 2222 | 968.75 2223 | 966.299988 2224 | 1005.75 2225 | 952.77002 2226 | 904.880005 2227 | 930.98999 2228 | 919.210022 2229 | 898.950012 2230 | 852.299988 2231 | 911.289978 2232 | 873.289978 2233 | 850.75 2234 | 859.119995 2235 | 806.580017 2236 | 752.440002 2237 | 800.030029 2238 | 851.809998 2239 | 857.390015 2240 | 887.679993 2241 | 896.23999 2242 | 816.210022 2243 | 848.809998 2244 | 870.73999 2245 | 845.219971 2246 | 876.070007 2247 | 909.700012 2248 | 888.669983 2249 | 899.23999 2250 | 873.590027 2251 | 879.72998 2252 | 868.570007 2253 | 913.179993 2254 | 904.419983 2255 | 885.280029 2256 | 887.880005 2257 | 871.630005 2258 | 863.159973 2259 | 868.150024 2260 | 872.799988 2261 | 869.419983 2262 | 890.640015 2263 | 903.25 2264 | 931.799988 2265 | 927.450012 2266 | 934.700012 2267 | 906.650024 2268 | 909.72998 2269 | 890.349976 2270 | 870.26001 2271 | 871.789978 2272 | 842.619995 2273 | 843.73999 2274 | 850.119995 2275 | 805.219971 2276 | 840.23999 2277 | 827.5 2278 | 831.950012 2279 | 836.570007 2280 | 845.710022 2281 | 874.090027 2282 | 845.140015 2283 | 825.880005 2284 | 825.440002 2285 | 838.51001 2286 | 832.22998 2287 | 845.849976 2288 | 868.599976 2289 | 869.890015 2290 | 827.159973 2291 | 833.73999 2292 | 835.190002 2293 | 826.840027 2294 | 789.169983 2295 | 788.419983 2296 | 778.940002 2297 | 770.049988 2298 | 743.330017 2299 | 773.140015 2300 | 764.900024 2301 | 752.830017 2302 | 735.090027 2303 | 700.820007 2304 | 696.330017 2305 | 712.869995 2306 | 682.549988 2307 | 683.380005 2308 | 676.530029 2309 | 719.599976 2310 | 721.359985 2311 | 750.73999 2312 | 756.549988 2313 | 753.890015 2314 | 778.119995 2315 | 794.349976 2316 | 784.039978 2317 | 768.539978 2318 | 822.919983 2319 | 806.119995 2320 | 813.880005 2321 | 832.859985 2322 | 815.940002 2323 | 787.530029 2324 | 797.869995 2325 | 811.080017 2326 | 834.380005 2327 | 842.5 2328 | 835.47998 2329 | 815.549988 2330 | 825.159973 2331 | 856.559998 2332 | 858.72998 2333 | 841.5 2334 | 852.059998 2335 | 865.299988 2336 | 869.599976 2337 | 832.390015 2338 | 850.080017 2339 | 843.549988 2340 | 851.919983 2341 | 866.22998 2342 | 857.51001 2343 | 855.159973 2344 | 873.640015 2345 | 872.809998 2346 | 877.52002 2347 | 907.23999 2348 | 903.799988 2349 | 919.530029 2350 | 907.390015 2351 | 929.22998 2352 | 909.23999 2353 | 908.349976 2354 | 883.919983 2355 | 893.070007 2356 | 882.880005 2357 | 909.710022 2358 | 908.130005 2359 | 903.469971 2360 | 888.330017 2361 | 887 2362 | 910.330017 2363 | 893.059998 2364 | 906.830017 2365 | 919.140015 2366 | 942.869995 2367 | 944.73999 2368 | 931.76001 2369 | 942.460022 2370 | 940.090027 2371 | 939.140015 2372 | 942.429993 2373 | 939.150024 2374 | 944.890015 2375 | 946.210022 2376 | 923.719971 2377 | 911.969971 2378 | 910.710022 2379 | 918.369995 2380 | 921.22998 2381 | 893.039978 2382 | 895.099976 2383 | 900.940002 2384 | 920.26001 2385 | 918.900024 2386 | 927.22998 2387 | 919.320007 2388 | 923.330017 2389 | 896.419983 2390 | 898.719971 2391 | 881.030029 2392 | 879.559998 2393 | 882.679993 2394 | 879.130005 2395 | 901.049988 2396 | 905.840027 2397 | 932.679993 2398 | 940.73999 2399 | 940.380005 2400 | 951.130005 2401 | 954.580017 2402 | 954.070007 2403 | 976.289978 2404 | 979.26001 2405 | 982.179993 2406 | 979.619995 2407 | 975.150024 2408 | 986.75 2409 | 987.47998 2410 | 1002.630005 2411 | 1005.650024 2412 | 1002.719971 2413 | 997.080017 2414 | 1010.47998 2415 | 1007.099976 2416 | 994.349976 2417 | 1005.809998 2418 | 1012.72998 2419 | 1004.090027 2420 | 979.72998 2421 | 989.669983 2422 | 996.460022 2423 | 1007.369995 2424 | 1026.130005 2425 | 1025.569946 2426 | 1028 2427 | 1028.119995 2428 | 1030.97998 2429 | 1028.930054 2430 | 1020.619995 2431 | 998.039978 2432 | 994.75 2433 | 1003.23999 2434 | 1016.400024 2435 | 1025.390015 2436 | 1033.369995 2437 | 1044.140015 2438 | 1042.72998 2439 | 1049.339966 2440 | 1052.630005 2441 | 1068.76001 2442 | 1065.48999 2443 | 1068.300049 2444 | 1064.660034 2445 | 1071.660034 2446 | 1060.869995 2447 | 1050.780029 2448 | 1044.380005 2449 | 1062.97998 2450 | 1060.609985 2451 | 1057.079956 2452 | 1029.849976 2453 | 1025.209961 2454 | 1040.459961 2455 | 1054.719971 2456 | 1057.579956 2457 | 1065.47998 2458 | 1071.48999 2459 | 1076.189941 2460 | 1073.189941 2461 | 1092.02002 2462 | 1096.560059 2463 | 1087.680054 2464 | 1097.910034 2465 | 1091.060059 2466 | 1081.400024 2467 | 1092.910034 2468 | 1079.599976 2469 | 1066.949951 2470 | 1063.410034 2471 | 1042.630005 2472 | 1066.109985 2473 | 1036.189941 2474 | 1042.880005 2475 | 1045.410034 2476 | 1046.5 2477 | 1066.630005 2478 | 1069.300049 2479 | 1093.079956 2480 | 1093.01001 2481 | 1098.51001 2482 | 1087.23999 2483 | 1093.47998 2484 | 1109.300049 2485 | 1110.319946 2486 | 1109.800049 2487 | 1094.900024 2488 | 1091.380005 2489 | 1106.23999 2490 | 1105.650024 2491 | 1110.630005 2492 | 1091.48999 2493 | 1095.630005 2494 | 1108.859985 2495 | 1109.23999 2496 | 1099.920044 2497 | 1105.97998 2498 | 1103.25 2499 | 1091.939941 2500 | 1095.949951 2501 | 1102.349976 2502 | 1106.410034 2503 | 1114.109985 2504 | 1107.930054 2505 | 1109.180054 2506 | 1096.079956 2507 | 1102.469971 2508 | 1114.050049 2509 | 1118.02002 2510 | 1120.589966 2511 | 1126.47998 2512 | 1127.780029 2513 | 1126.199951 2514 | 1126.420044 2515 | 1115.099976 2516 | 1132.98999 2517 | 1136.52002 2518 | 1137.140015 2519 | 1141.689941 2520 | 1144.97998 2521 | 1146.97998 2522 | 1136.219971 2523 | 1145.680054 2524 | 1148.459961 2525 | 1136.030029 2526 | 1150.22998 2527 | 1138.040039 2528 | 1116.47998 2529 | 1091.76001 2530 | 1096.780029 2531 | 1092.170044 2532 | 1097.5 2533 | 1084.530029 2534 | 1073.869995 2535 | 1089.189941 2536 | 1103.319946 2537 | 1097.280029 2538 | 1063.109985 2539 | 1066.189941 2540 | 1056.73999 2541 | 1070.52002 2542 | 1068.130005 2543 | 1078.469971 2544 | 1075.51001 2545 | 1094.869995 2546 | 1099.51001 2547 | 1106.75 2548 | 1109.170044 2549 | 1108.01001 2550 | 1094.599976 2551 | 1105.23999 2552 | 1102.939941 2553 | 1104.48999 2554 | 1115.709961 2555 | 1118.310059 2556 | 1118.790039 2557 | 1122.969971 2558 | 1138.699951 2559 | 1138.5 2560 | 1140.449951 2561 | 1145.609985 2562 | 1150.23999 2563 | 1149.98999 2564 | 1150.51001 2565 | 1159.459961 2566 | 1166.209961 2567 | 1165.829956 2568 | 1159.900024 2569 | 1165.810059 2570 | 1174.170044 2571 | 1167.719971 2572 | 1165.72998 2573 | 1166.589966 2574 | 1173.219971 2575 | 1173.27002 2576 | 1169.430054 2577 | 1178.099976 2578 | 1187.439941 2579 | 1189.439941 2580 | 1182.449951 2581 | 1186.439941 2582 | 1194.369995 2583 | 1196.47998 2584 | 1197.300049 2585 | 1210.650024 2586 | 1211.670044 2587 | 1192.130005 2588 | 1197.52002 2589 | 1207.170044 2590 | 1205.939941 2591 | 1208.670044 2592 | 1217.280029 2593 | 1212.050049 2594 | 1183.709961 2595 | 1191.359985 2596 | 1206.780029 2597 | 1186.689941 2598 | 1202.26001 2599 | 1173.599976 2600 | 1165.869995 2601 | 1128.150024 2602 | 1110.880005 2603 | 1159.72998 2604 | 1155.790039 2605 | 1171.670044 2606 | 1157.439941 2607 | 1135.680054 2608 | 1136.939941 2609 | 1120.800049 2610 | 1115.050049 2611 | 1071.589966 2612 | 1087.689941 2613 | 1073.650024 2614 | 1074.030029 2615 | 1067.949951 2616 | 1103.060059 2617 | 1089.410034 2618 | 1070.709961 2619 | 1098.380005 2620 | 1102.829956 2621 | 1064.880005 2622 | 1050.469971 2623 | 1062 2624 | 1055.689941 2625 | 1086.839966 2626 | 1091.599976 2627 | 1089.630005 2628 | 1115.22998 2629 | 1114.609985 2630 | 1116.040039 2631 | 1117.51001 2632 | 1113.199951 2633 | 1095.310059 2634 | 1092.040039 2635 | 1073.689941 2636 | 1076.76001 2637 | 1074.569946 2638 | 1041.23999 2639 | 1030.709961 2640 | 1027.369995 2641 | 1022.580017 2642 | 1028.060059 2643 | 1060.27002 2644 | 1070.25 2645 | 1077.959961 2646 | 1078.75 2647 | 1095.339966 2648 | 1095.170044 2649 | 1096.47998 2650 | 1064.880005 2651 | 1071.25 2652 | 1083.47998 2653 | 1069.589966 2654 | 1093.670044 2655 | 1102.660034 2656 | 1115.01001 2657 | 1113.839966 2658 | 1106.130005 2659 | 1101.530029 2660 | 1101.599976 2661 | 1125.859985 2662 | 1120.459961 2663 | 1127.23999 2664 | 1125.810059 2665 | 1121.640015 2666 | 1127.790039 2667 | 1121.060059 2668 | 1089.469971 2669 | 1083.609985 2670 | 1079.25 2671 | 1079.380005 2672 | 1092.540039 2673 | 1094.160034 2674 | 1075.630005 2675 | 1071.689941 2676 | 1067.359985 2677 | 1051.869995 2678 | 1055.329956 2679 | 1047.219971 2680 | 1064.589966 2681 | 1048.920044 2682 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1197.75 2736 | 1178.339966 2737 | 1178.589966 2738 | 1196.689941 2739 | 1199.72998 2740 | 1197.839966 2741 | 1180.72998 2742 | 1198.349976 2743 | 1189.400024 2744 | 1187.76001 2745 | 1180.550049 2746 | 1206.069946 2747 | 1221.530029 2748 | 1224.709961 2749 | 1223.119995 2750 | 1223.75 2751 | 1228.280029 2752 | 1233 2753 | 1240.400024 2754 | 1240.459961 2755 | 1241.589966 2756 | 1235.22998 2757 | 1242.869995 2758 | 1243.910034 2759 | 1247.079956 2760 | 1254.599976 2761 | 1258.839966 2762 | 1256.77002 2763 | 1257.540039 2764 | 1258.51001 2765 | 1259.780029 2766 | 1257.880005 2767 | 1257.640015 2768 | 1271.869995 2769 | 1270.199951 2770 | 1276.560059 2771 | 1273.849976 2772 | 1271.5 2773 | 1269.75 2774 | 1274.47998 2775 | 1285.959961 2776 | 1283.76001 2777 | 1293.23999 2778 | 1295.02002 2779 | 1281.920044 2780 | 1280.26001 2781 | 1283.349976 2782 | 1290.839966 2783 | 1291.180054 2784 | 1296.630005 2785 | 1299.540039 2786 | 1276.339966 2787 | 1286.119995 2788 | 1307.589966 2789 | 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1781.560059 3539 | 1792.5 3540 | 1774.199951 3541 | 1794.189941 3542 | 1782.589966 3543 | 1741.890015 3544 | 1755.199951 3545 | 1751.640015 3546 | 1773.430054 3547 | 1797.02002 3548 | 1799.839966 3549 | 1819.75 3550 | 1819.26001 3551 | 1829.829956 3552 | 1838.630005 3553 | 1840.76001 3554 | 1828.75 3555 | 1839.780029 3556 | 1836.25 3557 | 1847.609985 3558 | 1845.119995 3559 | 1845.160034 3560 | 1854.290039 3561 | 1859.449951 3562 | 1845.72998 3563 | 1873.910034 3564 | 1873.810059 3565 | 1877.030029 3566 | 1878.040039 3567 | 1877.170044 3568 | 1867.630005 3569 | 1868.199951 3570 | 1846.339966 3571 | 1841.130005 3572 | 1858.829956 3573 | 1872.25 3574 | 1860.77002 3575 | 1872.01001 3576 | 1866.52002 3577 | 1857.439941 3578 | 1865.619995 3579 | 1852.560059 3580 | 1849.040039 3581 | 1857.619995 3582 | 1872.339966 3583 | 1885.52002 3584 | 1890.900024 3585 | 1888.77002 3586 | 1865.089966 3587 | 1845.040039 3588 | 1851.959961 3589 | 1872.180054 3590 | 1833.079956 3591 | 1815.689941 3592 | 1830.609985 3593 | 1842.97998 3594 | 1862.310059 3595 | 1864.849976 3596 | 1871.890015 3597 | 1879.550049 3598 | 1875.390015 3599 | 1878.609985 3600 | 1863.400024 3601 | 1869.430054 3602 | 1878.329956 3603 | 1883.949951 3604 | 1883.680054 3605 | 1881.140015 3606 | 1884.660034 3607 | 1867.719971 3608 | 1878.209961 3609 | 1875.630005 3610 | 1878.47998 3611 | 1896.650024 3612 | 1897.449951 3613 | 1888.530029 3614 | 1870.849976 3615 | 1877.859985 3616 | 1885.079956 3617 | 1872.829956 3618 | 1888.030029 3619 | 1892.48999 3620 | 1900.530029 3621 | 1911.910034 3622 | 1909.780029 3623 | 1920.030029 3624 | 1923.569946 3625 | 1924.969971 3626 | 1924.23999 3627 | 1927.880005 3628 | 1940.459961 3629 | 1949.439941 3630 | 1951.27002 3631 | 1950.790039 3632 | 1943.890015 3633 | 1930.109985 3634 | 1936.160034 3635 | 1937.780029 3636 | 1941.98999 3637 | 1956.97998 3638 | 1959.47998 3639 | 1962.869995 3640 | 1962.609985 3641 | 1949.97998 3642 | 1959.530029 3643 | 1957.219971 3644 | 1960.959961 3645 | 1960.22998 3646 | 1973.319946 3647 | 1974.619995 3648 | 1985.439941 3649 | 1977.650024 3650 | 1963.709961 3651 | 1972.829956 3652 | 1964.680054 3653 | 1967.569946 3654 | 1977.099976 3655 | 1973.280029 3656 | 1981.569946 3657 | 1958.119995 3658 | 1978.219971 3659 | 1973.630005 3660 | 1983.530029 3661 | 1987.01001 3662 | 1987.97998 3663 | 1978.339966 3664 | 1978.910034 3665 | 1969.949951 3666 | 1970.069946 3667 | 1930.670044 3668 | 1925.150024 3669 | 1938.98999 3670 | 1920.209961 3671 | 1920.23999 3672 | 1909.569946 3673 | 1931.589966 3674 | 1936.920044 3675 | 1933.75 3676 | 1946.719971 3677 | 1955.180054 3678 | 1955.060059 3679 | 1971.73999 3680 | 1981.599976 3681 | 1986.51001 3682 | 1992.369995 3683 | 1988.400024 3684 | 1997.920044 3685 | 2000.02002 3686 | 2000.119995 3687 | 1996.73999 3688 | 2003.369995 3689 | 2002.280029 3690 | 2000.719971 3691 | 1997.650024 3692 | 2007.709961 3693 | 2001.540039 3694 | 1988.439941 3695 | 1995.689941 3696 | 1997.449951 3697 | 1985.540039 3698 | 1984.130005 3699 | 1998.97998 3700 | 2001.569946 3701 | 2011.359985 3702 | 2010.400024 3703 | 1994.290039 3704 | 1982.77002 3705 | 1998.300049 3706 | 1965.98999 3707 | 1982.849976 3708 | 1977.800049 3709 | 1972.290039 3710 | 1946.160034 3711 | 1946.170044 3712 | 1967.900024 3713 | 1964.819946 3714 | 1935.099976 3715 | 1968.890015 3716 | 1928.209961 3717 | 1906.130005 3718 | 1874.73999 3719 | 1877.699951 3720 | 1862.48999 3721 | 1862.76001 3722 | 1886.76001 3723 | 1904.01001 3724 | 1941.280029 3725 | 1927.109985 3726 | 1950.819946 3727 | 1964.579956 3728 | 1961.630005 3729 | 1985.050049 3730 | 1982.300049 3731 | 1994.650024 3732 | 2018.050049 3733 | 2017.810059 3734 | 2012.099976 3735 | 2023.569946 3736 | 2031.209961 3737 | 2031.920044 3738 | 2038.26001 3739 | 2039.680054 3740 | 2038.25 3741 | 2039.329956 3742 | 2039.819946 3743 | 2041.319946 3744 | 2051.800049 3745 | 2048.719971 3746 | 2052.75 3747 | 2063.5 3748 | 2069.409912 3749 | 2067.030029 3750 | 2072.830078 3751 | 2067.560059 3752 | 2053.439941 3753 | 2066.550049 3754 | 2074.330078 3755 | 2071.919922 3756 | 2075.370117 3757 | 2060.310059 3758 | 2059.820068 3759 | 2026.140015 3760 | 2035.329956 3761 | 2002.329956 3762 | 1989.630005 3763 | 1972.73999 3764 | 2012.890015 3765 | 2061.22998 3766 | 2070.649902 3767 | 2078.540039 3768 | 2082.169922 3769 | 2081.879883 3770 | 2088.77002 3771 | 2090.570068 3772 | 2080.350098 3773 | 2058.899902 3774 | 2058.199951 3775 | 2020.579956 3776 | 2002.609985 3777 | 2025.900024 3778 | 2062.139893 3779 | 2044.810059 3780 | 2028.26001 3781 | 2023.030029 3782 | 2011.27002 3783 | 1992.670044 3784 | 2019.420044 3785 | 2022.550049 3786 | 2032.119995 3787 | 2063.149902 3788 | 2051.820068 3789 | 2057.090088 3790 | 2029.550049 3791 | 2002.160034 3792 | 2021.25 3793 | 1994.98999 3794 | 2020.849976 3795 | 2050.030029 3796 | 2041.51001 3797 | 2062.52002 3798 | 2055.469971 3799 | 2046.73999 3800 | 2068.590088 3801 | 2068.530029 3802 | 2088.47998 3803 | 2096.98999 3804 | 2100.340088 3805 | 2099.679932 3806 | 2097.449951 3807 | 2110.300049 3808 | 2109.659912 3809 | 2115.47998 3810 | 2113.860107 3811 | 2110.73999 3812 | 2104.5 3813 | 2117.389893 3814 | 2107.780029 3815 | 2098.530029 3816 | 2101.040039 3817 | 2071.26001 3818 | 2079.429932 3819 | 2044.160034 3820 | 2040.23999 3821 | 2065.949951 3822 | 2053.399902 3823 | 2081.189941 3824 | 2074.280029 3825 | 2099.5 3826 | 2089.27002 3827 | 2108.100098 3828 | 2104.419922 3829 | 2091.5 3830 | 2061.050049 3831 | 2056.149902 3832 | 2061.02002 3833 | 2086.23999 3834 | 2067.889893 3835 | 2059.689941 3836 | 2066.959961 3837 | 2080.620117 3838 | 2076.330078 3839 | 2081.899902 3840 | 2091.179932 3841 | 2102.060059 3842 | 2092.429932 3843 | 2095.840088 3844 | 2106.629883 3845 | 2104.98999 3846 | 2081.179932 3847 | 2100.399902 3848 | 2097.290039 3849 | 2107.959961 3850 | 2112.929932 3851 | 2117.689941 3852 | 2108.919922 3853 | 2114.76001 3854 | 2106.850098 3855 | 2085.51001 3856 | 2108.290039 3857 | 2114.48999 3858 | 2089.459961 3859 | 2080.149902 3860 | 2088 3861 | 2116.100098 3862 | 2105.330078 3863 | 2099.120117 3864 | 2098.47998 3865 | 2121.100098 3866 | 2122.72998 3867 | 2129.199951 3868 | 2127.830078 3869 | 2125.850098 3870 | 2130.820068 3871 | 2126.060059 3872 | 2104.199951 3873 | 2123.47998 3874 | 2120.790039 3875 | 2107.389893 3876 | 2111.72998 3877 | 2109.600098 3878 | 2114.070068 3879 | 2095.840088 3880 | 2092.830078 3881 | 2079.280029 3882 | 2080.149902 3883 | 2105.199951 3884 | 2108.860107 3885 | 2094.110107 3886 | 2084.429932 3887 | 2096.290039 3888 | 2100.439941 3889 | 2121.23999 3890 | 2109.98999 3891 | 2122.850098 3892 | 2124.199951 3893 | 2108.580078 3894 | 2102.310059 3895 | 2101.48999 3896 | 2057.639893 3897 | 2063.110107 3898 | 2077.419922 3899 | 2076.780029 3900 | 2068.76001 3901 | 2081.340088 3902 | 2046.680054 3903 | 2051.310059 3904 | 2076.620117 3905 | 2099.600098 3906 | 2108.949951 3907 | 2107.399902 3908 | 2124.290039 3909 | 2126.639893 3910 | 2128.280029 3911 | 2119.209961 3912 | 2114.149902 3913 | 2102.149902 3914 | 2079.649902 3915 | 2067.639893 3916 | 2093.25 3917 | 2108.570068 3918 | 2108.629883 3919 | 2103.840088 3920 | 2098.040039 3921 | 2093.320068 3922 | 2099.840088 3923 | 2083.560059 3924 | 2077.570068 3925 | 2104.179932 3926 | 2084.070068 3927 | 2086.050049 3928 | 2083.389893 3929 | 2091.540039 3930 | 2102.439941 3931 | 2096.919922 3932 | 2079.610107 3933 | 2035.72998 3934 | 1970.890015 3935 | 1893.209961 3936 | 1867.609985 3937 | 1940.51001 3938 | 1987.660034 3939 | 1988.869995 3940 | 1972.180054 3941 | 1913.849976 3942 | 1948.859985 3943 | 1951.130005 3944 | 1921.219971 3945 | 1969.410034 3946 | 1942.040039 3947 | 1952.290039 3948 | 1961.050049 3949 | 1953.030029 3950 | 1978.089966 3951 | 1995.310059 3952 | 1990.199951 3953 | 1958.030029 3954 | 1966.969971 3955 | 1942.73999 3956 | 1938.76001 3957 | 1932.23999 3958 | 1931.339966 3959 | 1881.77002 3960 | 1884.089966 3961 | 1920.030029 3962 | 1923.819946 3963 | 1951.359985 3964 | 1987.050049 3965 | 1979.920044 3966 | 1995.829956 3967 | 2013.430054 3968 | 2014.890015 3969 | 2017.459961 3970 | 2003.689941 3971 | 1994.23999 3972 | 2023.859985 3973 | 2033.109985 3974 | 2033.660034 3975 | 2030.77002 3976 | 2018.939941 3977 | 2052.51001 3978 | 2075.149902 3979 | 2071.179932 3980 | 2065.889893 3981 | 2090.350098 3982 | 2089.409912 3983 | 2079.360107 3984 | 2104.050049 3985 | 2109.790039 3986 | 2102.310059 3987 | 2099.929932 3988 | 2099.199951 3989 | 2078.580078 3990 | 2081.719971 3991 | 2075 3992 | 2045.969971 3993 | 2023.040039 3994 | 2053.189941 3995 | 2050.439941 3996 | 2083.580078 3997 | 2081.23999 3998 | 2089.169922 3999 | 2086.590088 4000 | 2089.139893 4001 | 2088.870117 4002 | 2090.110107 4003 | 2080.409912 4004 | 2102.629883 4005 | 2079.51001 4006 | 2049.620117 4007 | 2091.689941 4008 | 2077.070068 4009 | 2063.590088 4010 | 2047.619995 4011 | 2052.22998 4012 | 2012.369995 4013 | 2021.939941 4014 | 2043.410034 4015 | 2073.070068 4016 | 2041.890015 4017 | 2005.550049 4018 | 2021.150024 4019 | 2038.969971 4020 | 2064.290039 4021 | 2060.98999 4022 | 2056.5 4023 | 2078.360107 4024 | 2063.360107 4025 | 2043.939941 4026 | 2012.660034 4027 | 2016.709961 4028 | 1990.26001 4029 | 1943.089966 4030 | 1922.030029 4031 | 1923.670044 4032 | 1938.680054 4033 | 1890.280029 4034 | 1921.839966 4035 | 1880.329956 4036 | 1881.329956 4037 | 1859.329956 4038 | 1868.98999 4039 | 1906.900024 4040 | 1877.079956 4041 | 1903.630005 4042 | 1882.949951 4043 | 1893.359985 4044 | 1940.23999 4045 | 1939.380005 4046 | 1903.030029 4047 | 1912.530029 4048 | 1915.449951 4049 | 1880.050049 4050 | 1853.439941 4051 | 1852.209961 4052 | 1851.859985 4053 | 1829.079956 4054 | 1864.780029 4055 | 1895.579956 4056 | 1926.819946 4057 | 1917.829956 4058 | 1917.780029 4059 | 1945.5 4060 | 1921.27002 4061 | 1929.800049 4062 | 1951.699951 4063 | 1948.050049 4064 | 1932.22998 4065 | 1978.349976 4066 | 1986.449951 4067 | 1993.400024 4068 | 1999.98999 4069 | 2001.76001 4070 | 1979.26001 4071 | 1989.26001 4072 | 1989.569946 4073 | 2022.189941 4074 | 2019.640015 4075 | 2015.930054 4076 | 2027.219971 4077 | 2040.589966 4078 | 2049.580078 4079 | 2051.600098 4080 | 2049.800049 4081 | 2036.709961 4082 | 2035.939941 4083 | 2037.050049 4084 | 2055.01001 4085 | 2063.949951 4086 | 2059.73999 4087 | 2072.780029 4088 | 2066.129883 4089 | 2045.170044 4090 | 2066.659912 4091 | 2041.910034 4092 | 2047.599976 4093 | 2041.98999 4094 | 2061.719971 4095 | 2082.419922 4096 | 2082.780029 4097 | 2080.72998 4098 | 2094.340088 4099 | 2100.800049 4100 | 2102.399902 4101 | 2091.47998 4102 | 2091.580078 4103 | 2087.790039 4104 | 2091.699951 4105 | 2095.149902 4106 | 2075.810059 4107 | 2065.300049 4108 | 2081.429932 4109 | 2063.370117 4110 | 2051.120117 4111 | 2050.629883 4112 | 2057.139893 4113 | 2058.689941 4114 | 2084.389893 4115 | 2064.459961 4116 | 2064.110107 4117 | 2046.609985 4118 | 2066.659912 4119 | 2047.209961 4120 | 2047.630005 4121 | 2040.040039 4122 | 2052.320068 4123 | 2048.040039 4124 | 2076.060059 4125 | 2090.540039 4126 | 2090.100098 4127 | 2099.060059 4128 | 2096.949951 4129 | 2099.330078 4130 | 2105.26001 4131 | 2099.129883 4132 | 2109.409912 4133 | 2112.129883 4134 | 2119.120117 4135 | 2115.47998 4136 | 2096.070068 4137 | 2079.060059 4138 | 2075.320068 4139 | 2071.5 4140 | 2077.98999 4141 | 2071.219971 4142 | 2083.25 4143 | 2088.899902 4144 | 2085.449951 4145 | 2113.320068 4146 | 2037.410034 4147 | 2000.540039 4148 | 2036.089966 4149 | 2070.77002 4150 | 2098.860107 4151 | 2102.949951 4152 | 2088.550049 4153 | 2099.72998 4154 | 2097.899902 4155 | 2129.899902 4156 | 2137.159912 4157 | 2152.139893 4158 | 2152.429932 4159 | 2163.75 4160 | 2161.73999 4161 | 2166.889893 4162 | 2163.780029 4163 | 2173.02002 4164 | 2165.169922 4165 | 2175.030029 4166 | 2168.47998 4167 | 2169.179932 4168 | 2166.580078 4169 | 2170.060059 4170 | 2173.600098 4171 | 2170.840088 4172 | --------------------------------------------------------------------------------