huseinzol05

import urllib

print 'downloading crude oil data'
urllib.urlretrieve("https://fred.stlouisfed.org/graph/fredgraph.csv?chart_type=line&recession_bars=on&log_scales=&bgcolor=%23e1e9f0&graph_bgcolor=%23ffffff&fo=Open+Sans&ts=12&tts=12&txtcolor=%23444444&show_legend=yes&show_axis_titles=yes&height=450&stacking=&range=1yr&mode=fred&id=DCOILWTICO&transformation=lin&nd=1986-01-02&ost=-99999&oet=99999&lsv=&lev=&mma=0&fml=a&fgst=lin&fgsnd=2009-06-01&fq=Daily&fam=avg&vintage_date=&revision_date=&line_color=%234572a7&line_style=solid&lw=2&scale=left&mark_type=none&mw=2&width=1168", "crude-oil.csv")
print 'downloading diesel data'
urllib.urlretrieve("https://fred.stlouisfed.org/graph/fredgraph.csv?chart_type=line&recession_bars=on&log_scales=&bgcolor=%23e1e9f0&graph_bgcolor=%23ffffff&fo=Open+Sans&ts=12&tts=12&txtcolor=%23444444&show_legend=yes&show_axis_titles=yes&drp=0&cosd=2017-01-08&coed=2018-01-08&height=450&stacking=&range=1yr&mode=fred&id=DDFUELUSGULF&transformation=lin&nd=2006-06-14&ost=-99999&oet=99999&lsv=&lev=&

huseinzol05

import numpy as np
from sklearn.ensemble import *
import xgboost as xgb
from sklearn.cross_validation import train_test_split

X = np.random.uniform(size=(100,10))
Y = np.random.uniform(size=(100))

# split our dataset for validation
train_X, test_X, train_Y, test_Y = train_test_split(X, Y, test_size = 0.2)

huseinzol05

import speechpy
import scipy.io.wavfile as wav
import numpy as np

def extract_features(signal, fs):
    frames = speechpy.processing.stack_frames(signal, sampling_frequency=fs, frame_length=0.020, frame_stride=0.01, filter=lambda x: np.ones((x,)),zero_padding=True)
    power_spectrum = speechpy.processing.power_spectrum(frames, fft_points=1)
    logenergy = speechpy.feature.lmfe(signal, sampling_frequency=fs, frame_length=0.020, frame_stride=0.01,num_filters=1, fft_length=512, low_frequency=0, high_frequency=None)
    mfcc = speechpy.feature.mfcc(signal, sampling_frequency=fs, frame_length=0.020, frame_stride=0.01,num_filters=1, fft_length=512, low_frequency=0, high_frequency=None)
    mfcc_cmvn = speechpy.processing.cmvnw(mfcc,win_size=301,variance_normalization=True)

huseinzol05

x = np.random.randint(0, 100, size=(100))
rects1=plt.bar(np.arange(x.shape[0]), x)
def autolabel(rects):
    (y_bottom, y_top) = plt.ylim()
    y_height = y_top - y_bottom
    for rect in rects:
        height = rect.get_height()
        p_height = (height / y_height)
        if p_height > 0.95:
            label_position = height - (y_height * 0.05)

huseinzol05

import numpy as np
import tensorflow as tf

class Feed_forward:
    def __init__(self, input_dimension):
        self.X = tf.placeholder(tf.float32, [None, input_dimension])
        self.Y = tf.placeholder(tf.float32, [None, 1])
        hidden_layer = tf.Variable(tf.random_uniform([input_dimension, 1], -1.0, 1.0))
        logits = tf.matmul(self.X, hidden_layer)
        self.cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = logits, labels = self.Y))

huseinzol05

import numpy as np
import tensorflow as tf

class Model:
    def __init__(self):
        def conv_layer(x, conv, stride = 1):
            return tf.nn.conv2d(x, conv, [1, stride, stride, 1], padding = 'SAME')
        def pooling(x, k = 2, stride = 2):
            return tf.nn.max_pool(x, ksize = [1, k, k, 1], strides = [1, stride, stride, 1], padding = 'SAME')
        self.X = tf.placeholder(tf.float32, [None, 80, 80, 4])

huseinzol05

huseinzol05

huseinzol05

huseinzol05