| def interactive_histogram(df,col,n_bins,bin_range,title,x_axis_label,x_tooltip): | |
| """Plot interactive histogram using bokeh. | |
| df: pandas dataframe | |
| col: column of panda dataframe to plot (eg. age of users) | |
| n_bins: number of bins, e.g. 9 | |
| bin_range: list with min and max value. e.g. [10,100] age of users. | |
| title: title of plot. e.g. 'Airnb Users Age Distribution' | |
| x_axis_label: x axis label. e.g. 'Age (years)'. | |
| x_tooltip: x axis tooltip string. e.g. 'Age' |
| ## Add this to the first block in your note book | |
| import uuid | |
| from IPython.core.display import display, HTML | |
| import json | |
| class RenderJSON(object): | |
| def __init__(self, json_data): | |
| if isinstance(json_data, dict): | |
| self.json_str = json.dumps(json_data) |
| -- multiple learning rates per network. Optimizes two copies of a model network and checks if the optimization steps (2) and (3) produce the same weights/parameters. | |
| require 'torch' | |
| require 'nn' | |
| require 'optim' | |
| torch.setdefaulttensortype('torch.FloatTensor') | |
| -- (1) Define a model for this example. | |
| local model = nn.Sequential() | |
| model:add(nn.Linear(10,20)) |
People
 :bowtie: |
😄 :smile: |
😆 :laughing: |
|---|---|---|
😊 :blush: |
😃 :smiley: |
:relaxed: |
😏 :smirk: |
😍 :heart_eyes: |
😘 :kissing_heart: |
😚 :kissing_closed_eyes: |
😳 :flushed: |
😌 :relieved: |
😆 :satisfied: |
😁 :grin: |
😉 :wink: |
😜 :stuck_out_tongue_winking_eye: |
😝 :stuck_out_tongue_closed_eyes: |
😀 :grinning: |
😗 :kissing: |
😙 :kissing_smiling_eyes: |
😛 :stuck_out_tongue: |
In python, you have floats and decimals that can be rounded. If you care about the accuracy of rounding, use decimal type. If you use floats, you will have issues with accuracy.
All the examples use demical types, except for the original value, which is automatically casted as a float.
To set the context of what we are working with, let's start with an original value.