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from process import *
import pandas as pd
import glob
import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, Flatten, MaxPool2D, Dense, Dropout


from random import shuffle

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'''Neural style transfer with Keras.
Run the script with:
```
python neural_style_transfer.py path_to_your_base_image.jpg path_to_your_reference.jpg prefix_for_results
```
e.g.:
```
python neural_style_transfer.py img/tuebingen.jpg img/starry_night.jpg results/my_result
```
Optional parameters:

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from sklearn.datasets import *
from sklearn import tree
import graphviz

wine = load_wine()
clf = tree.DecisionTreeClassifier() # init the tree
clf = clf.fit(wine.data, wine.target) # train the tree
# export the learned decision tree
dot_data = tree.export_graphviz(clf, out_file=None,
                         feature_names=wine.feature_names,

hhl60492

from sklearn.datasets import *
from sklearn import tree

from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize

n_classes = 3
wine = load_wine()
clf = tree.DecisionTreeClassifier()

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# read in the csv
df = pd.read_csv("eth-cad-max.csv")

# get the prices col slice from df
prices = np.array(df['price'])
foo = []

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# read in the csv
df = pd.read_csv("eth-cad-max.csv")

# get the prices col slice from df
prices = np.array(df['price'])
H = 0.55

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import numpy as np
import pandas as pd
import nolds
from fbm import FBM
import matplotlib.pyplot as plt

# number of time steps (days) to predict ahead
n = 30
# number of FBMs to realize
c = 1000