christfan868

# Create a new Prophet Object
prophet = Prophet(holidays=holidays,
                  growth='logistic',
                  seasonality_prior_scale=seasonality_prior_scale,
                  holidays_prior_scale=holidays_prior_scale)

# Training the model on the dataset
prophet.fit(trnDF)

# Create DataFrame of future dates

christfan868

While attempting to explain JavaScript's reduce method on arrays, conceptually, I came up with the following - hopefully it's helpful; happy to tweak it if anyone has suggestions.

Intro

JavaScript Arrays have lots of built in methods on their prototype. Some of them mutate - ie, they change the underlying array in-place. Luckily, most of them do not - they instead return an entirely distinct array. Since arrays are conceptually a contiguous list of items, it helps code clarity and maintainability a lot to be able to operate on them in a "functional" way. (I'll also insist on referring to an array as a "list" - although in some languages, List is a native data type, in JS and this post, I'm referring to the concept. Everywhere I use the word "list" you can assume I'm talking about a JS Array) This means, to perform a single operation on the list as a whole ("atomically"), and to return a new list - thus making it much simpler to think about both the old list and the new one, what they contain, and

christfan868

# What does the profile of someone who is likely to stay look like?
stay_df = df[df['left'] == 0]

# apply to multiple fns to multiple cols
retention_profile = pd.DataFrame(columns=[list(df)])  # Create a DataFrame with the same column names as the main DataFrame. 
retention_profile = retention_profile.drop('left', 1)  # Drop the 'left' column

# Populate the retention profile....
retention_profile = retention_profile.append({'satisfaction_level': stay_df['satisfaction_level'].mean(),
                                              'last_evaluation': stay_df['last_evaluation'].mean(),

christfan868

# 2. Define Model.
model = Sequential()
model.add(Dense(12, input_dim=9, init='normal', activation='relu'))
model.add(Dense(9, init='normal', activation='relu'))
model.add(Dense(6, init='normal', activation='relu'))
model.add(Dense(3, init='normal', activation='relu'))
model.add(Dense(1, init='normal', activation='sigmoid'))

# 3. Compile Model.
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

christfan868

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

christfan868

import nltk

text = """The Buddha, the Godhead, resides quite as comfortably in the circuits of a digital
computer or the gears of a cycle transmission as he does at the top of a mountain
or in the petals of a flower. To think otherwise is to demean the Buddha...which is
to demean oneself."""

# Used when tokenizing words
sentence_re = r'''(?x)      # set flag to allow verbose regexps
      ([A-Z])(\.[A-Z])+\.?  # abbreviations, e.g. U.S.A.