jennyonjourney

import tensorflow as tf
import numpy as np
tf.set_random_seed(777)  # for reproducibility

# Predicting animal type based on various features
xy = np.loadtxt('ted_main_ranking_viewer.csv', delimiter=',', dtype=np.float32)
X_data = xy[:, 0:-1]
def MinMaxScaler(x_data):
    numerator = x_data - np.min(x_data, 0)
    denominator = np.max(x_data, 0) - np.min(x_data, 0)

jennyonjourney

import tensorflow as tf
import numpy as np
tf.set_random_seed(777)  # for reproducibility
learning_rate = 0.0001
xy = np.loadtxt('multiTimeline_bitcoin_2_normalized.csv', delimiter=',', dtype=np.float32)
X_data = xy[:, 0:-1]
def MinMaxScaler(x_data):
    numerator = x_data - np.min(x_data, 0)
    denominator = np.max(x_data, 0) - np.min(x_data, 0)
    return numerator / (denominator + 1e-10)

jennyonjourney

# 10.2 Write a program to read through the mbox-short.txt and figure out the distribution by hour of the day for each of the messages. You can pull the hour out from the 'From ' line by finding the time and then splitting the string a second time using a colon.
#From [email protected] Sat Jan  5 09:14:16 2008
# Once you have accumulated the counts for each hour, print out the counts, sorted by hour as shown below.

name = input("Enter file:")
if len(name) < 1:
    name = "mbox-short.txt"
handle = open(name)

counts = dict()

jennyonjourney

import numpy as np
import pandas as pd
train = pd.read_csv("data/train (bike).csv")
print(train.shape)
train.head()
train = pd.read_csv("data/train (bike).csv", parse_dates=["datetime"])
print(train.shape)

## Data processing
train["year"] = train["datetime"].dt.year