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summary
With
python
batch_size = 128
num_hidden_nodes = 1024
beta = 1e-3
num_steps = 3001
Results
* Test accuracy: 88.5% with beta=0.000000 (no L2 regulization)
* Test accuracy: 86.7% with beta=0.000010
* Test accuracy: 88.8% with beta=0.000100
* Test accuracy: 92.6% with beta=0.001000
* Test accuracy: 89.7% with beta=... | offset = 0 #offset = (step * batch_size) % (train_labels.shape[0] - batch_size) | google_dl_udacity/lesson3/3_regularization.ipynb | jinzishuai/learn2deeplearn | gpl-3.0 |
With
python
batch_size = 128
num_hidden_nodes = 1024
beta = 1e-3
num_steps = 3001
Results
* Original Test accuracy: 92.6% with beta=0.001000
* With offset = 0: Test accuracy: 67.5% with beta=0.001000
Problem 3
Introduce Dropout on the hidden layer of the neural network. Remember: Dropout should only be introduced duri... | keep_rate = 0.5
dropout = tf.nn.dropout(activated_hidden_layer, keep_rate) #dropout if applied after activation
logits = tf.matmul(dropout, weights2) + biases2 | google_dl_udacity/lesson3/3_regularization.ipynb | jinzishuai/learn2deeplearn | gpl-3.0 |
Change the above cell to refer to the file locations on your computer (The reason it is two files is that I encountered a previously unseen error halfway through, and had to put a new try/except into the code and restart the scraping). | len(scrape1)
len(scrape2)
df = pd.concat([scrape1,scrape2])
len(df)
df.columns
df['days_jailed'] = df.release_timestamp - df.booking_timestamp
df['days_jailed_np'] = df.days_jailed.dt.days
df.loc[df['days_jailed_np']>7,'days_jailed_np'] = 7
sns.distplot(df['days_jailed_np'].dropna()) | src/visualization/Fulton County Data Viz.ipynb | lahoffm/aclu-bail-reform | mit |
This gives us the overall distribution of time imprisoned for everyone in our dataset who has been released. | df.groupby('inmate_race').agg({'days_jailed_np' : np.mean}).plot(kind='bar') | src/visualization/Fulton County Data Viz.ipynb | lahoffm/aclu-bail-reform | mit |
This gives us mean time in prison by race. | ax= sns.violinplot(data=df, x='inmate_race', y='days_jailed_np', cut=0, scale='width')
for tick in ax.get_xticklabels():
tick.set_rotation(45) | src/visualization/Fulton County Data Viz.ipynb | lahoffm/aclu-bail-reform | mit |
Get IRS data on businesses
The IRS website has some aggregated statistics on business returns in Excel files. We will use the Selected Income and Tax Items for Selected Years.
The original data is from the file linked here:
https://www.irs.gov/pub/irs-soi/14intaba.xls,
but I cleaned it up by hand to remove footnotes an... | raw = pd.read_excel('data/14intaba_cleaned.xls', skiprows=2) | notebooks/input_output.ipynb | tanyaschlusser/stats-via-python | mit |
Look at the last 3 rows
The function pd.read_excel returns an object called a 'Data Frame', that is defined inside of the Pandas library. It has associated functions that access and manipulate the data inside. For example: | # Look at the last 3 rows
raw.tail(3) | notebooks/input_output.ipynb | tanyaschlusser/stats-via-python | mit |
Split out the 'Current dollars' and 'Constant 1990 dollars'
There are two sets of data — for the actual dollars for each variable, and also for constant dollars (accounting for inflation). We will split the raw dataset into two and then index the rows by the units (whether they're number of returns or amount paid/claim... | index_cols = ['Units', 'Variable']
current_dollars_cols = index_cols + [
c for c in raw.columns if c.startswith('Current')
]
constant_dollars_cols = index_cols + [
c for c in raw.columns if c.startswith('Constant')
]
current_dollars_data = raw[current_dollars_cols][9:]
current_dollars_data.set_index(keys=index... | notebooks/input_output.ipynb | tanyaschlusser/stats-via-python | mit |
Statistics
Pandas provides methods for statistical summaries. The describe method gives an overall summary. dropna(axis=1) deletes columns containing null values. If it were axis=0 it would be deleting rows. | per_entry = (
constant_dollars_data.transpose()['Amount (thousand USD)'] * 1000 /
constant_dollars_data.transpose()['Number of returns']
)
per_entry.dropna(axis=1).describe().round() | notebooks/input_output.ipynb | tanyaschlusser/stats-via-python | mit |
Plot
The library that provides plot functions is called Matplotlib. To show the plots in this notebook you need to use the "magic method" %matplotlib inline. It should be used at the beginning of the notebook for clarity. | # This should always be at the beginning of the notebook,
# like all magic statements and import statements.
# It's only here because I didn't want to describe it earlier.
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (10, 12) | notebooks/input_output.ipynb | tanyaschlusser/stats-via-python | mit |
The per-entry data
The data are (I think) for every form filed, not really per capita, but since we're not interpreting it for anything important we can conflate the two.
Per capita income (Blue line) rose a lot with the tech bubble, then sunk with its crash, and then followed the housing bubble and crash. It also look... | styles = ['b-', 'g-.', 'r--', 'c-', 'm:']
axes = per_entry[[
'Total income',
'Total social security benefits (not in income)',
'Business or profession net income less loss',
'Total payments',
'Unemployment compensation']].plot(style=styles)
plt.suptitle('Average USD per return (w... | notebooks/input_output.ipynb | tanyaschlusser/stats-via-python | mit |
Also with log-y
We can see the total social security benefits payout (Green dot dash) increase as the baby boomers come of age, and we see the unemployment compensation (Magenta dots) spike after the 2008 crisis and then fall off. | styles = ['b-', 'r--', 'g-.', 'c-', 'm:']
axes = constant_dollars_data.transpose()['Amount (thousand USD)'][[
'Total income',
'Total payments',
'Total social security benefits (not in income)',
'Business or profession net income less loss',
'Unemployment compensation']].plot(logy... | notebooks/input_output.ipynb | tanyaschlusser/stats-via-python | mit |
Step 2: Load Training Data | print("Running on system: %s" % socket.gethostname())
if True:
# Using a local copy of data volume
#inDir = '/Users/graywr1/code/bio-segmentation/data/ISBI2012/'
inDir = '/home/pekalmj1/Data/EM_2012'
X = ndp.nddl.load_cube(os.path.join(inDir, 'train-volume.tif'))
Y = ndp.nddl.load_cube(os.path.joi... | examples/isbi2012_train.ipynb | neurodata/ndparse | apache-2.0 |
Step 3: Training | # Note that for demonstration purposes we use an artifically low
# number of training slices and epochs. For actualy training,
# you would use more data and train for longer.
train_slices = np.arange(2) # e.g. change to np.arange(25)
valid_slices = np.arange(25,30)
n_epochs = 1
tic = time.time()
model = ndp.nd... | examples/isbi2012_train.ipynb | neurodata/ndparse | apache-2.0 |
Next, we need to authenticate ourselves to Google Cloud Platform. If you are running the code cell below for the first time, a link will show up, which leads to a web page for authentication and authorization. Login with your crendentials and make sure the permissions it requests are proper, after clicking Allow button... | auth.authenticate_user() | datathon/nusdatathon18/tutorials/ddsm_ml_tutorial.ipynb | GoogleCloudPlatform/healthcare | apache-2.0 |
At the same time, let's set the project we are going to use throughout the tutorial. | project_id = 'nus-datathon-2018-team-00'
os.environ["GOOGLE_CLOUD_PROJECT"] = project_id | datathon/nusdatathon18/tutorials/ddsm_ml_tutorial.ipynb | GoogleCloudPlatform/healthcare | apache-2.0 |
Optional: In this Colab we can opt to use GPU to train our model by clicking "Runtime" on the top menus, then clicking "Change runtime type", select "GPU" for hardware accelerator. You can verify that GPU is working with the following code cell. | # Should output something like '/device:GPU:0'.
tf.test.gpu_device_name() | datathon/nusdatathon18/tutorials/ddsm_ml_tutorial.ipynb | GoogleCloudPlatform/healthcare | apache-2.0 |
Dataset
We have already extracted the images from the DICOM files to separate folders on GCS, and some preprocessing were also done with the raw images (If you need custom preprocessing, please consult our tutorial on image preprocessing).
The folders ending with _demo contain subsets of training and test images. Speci... | client = storage.Client()
bucket_name = 'datathon-cbis-ddsm-colab'
bucket = client.get_bucket(bucket_name)
def load_images(folder):
images = []
labels = []
# The image name is in format: <LABEL>_Calc_{Train,Test}_P_<Patient_ID>_{Left,Right}_CC.
for label in [1, 2, 3, 4]:
blobs = bucket.list_blobs(prefix=(... | datathon/nusdatathon18/tutorials/ddsm_ml_tutorial.ipynb | GoogleCloudPlatform/healthcare | apache-2.0 |
Let's create a model function, which will be passed to an estimator that we will create later. The model has an architecture of 6 layers:
Convolutional Layer: Applies 32 5x5 filters, with ReLU activation function
Pooling Layer: Performs max pooling with a 2x2 filter and stride of 2
Convolutional Layer: Applies 64 5x5 ... | KERNEL_SIZE = 5 #@param
DROPOUT_RATE = 0.25 #@param
def cnn_model_fn(features, labels, mode):
"""Model function for CNN."""
# Input Layer.
# Reshape to 4-D tensor: [batch_size, height, width, channels]
# DDSM images are grayscale, which have 1 channel.
input_layer = tf.reshape(features["x"], [-1, 95, 128, 1... | datathon/nusdatathon18/tutorials/ddsm_ml_tutorial.ipynb | GoogleCloudPlatform/healthcare | apache-2.0 |
Now that we have a model function, next step is feeding it to an estimator for training. Here are are creating a main function as required by tensorflow. | BATCH_SIZE = 20 #@param
STEPS = 1000 #@param
artifacts_bucket_name = 'nus-datathon-2018-team-00-shared-files'
# Append a random number to avoid collision.
artifacts_path = "ddsm_model_%s" % random.randint(0, 1000)
model_dir = "gs://%s/%s" % (artifacts_bucket_name, artifacts_path)
def main(_):
# Load training and te... | datathon/nusdatathon18/tutorials/ddsm_ml_tutorial.ipynb | GoogleCloudPlatform/healthcare | apache-2.0 |
Finally, here comes the exciting moment. We are going to train and evaluate the model we just built! Run the following code cell and pay attention to the accuracy printed at the end of logs.
Note if this is not the first time you run the following cell, to avoid weird errors like "NaN loss during training", please run ... | # Remove temporary files.
artifacts_bucket = client.get_bucket(artifacts_bucket_name)
artifacts_bucket.delete_blobs(artifacts_bucket.list_blobs(prefix=artifacts_path))
# Set logging level.
tf.logging.set_verbosity(tf.logging.INFO)
# Start training, this will call the main method defined above behind the scene.
# The ... | datathon/nusdatathon18/tutorials/ddsm_ml_tutorial.ipynb | GoogleCloudPlatform/healthcare | apache-2.0 |
UMAP vs T-SNE
Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. The algorithm is founded on three assumptions about the data
The data is uniformly distributed on a Riemannia... | corpus = load_hobbies() | examples/Sangarshanan/comparing_corpus_visualizers.ipynb | pdamodaran/yellowbrick | apache-2.0 |
Writing a Function to quickly Visualize Corpus
Which can then be used for rapid comparison | def visualize(dim_reduction,encoding,corpus,labels = True,alpha=0.7,metric=None):
if 'tfidf' in encoding.lower():
encode = TfidfVectorizer()
if 'count' in encoding.lower():
encode = CountVectorizer()
docs = encode.fit_transform(corpus.data)
if labels is True:
labels = corpus.t... | examples/Sangarshanan/comparing_corpus_visualizers.ipynb | pdamodaran/yellowbrick | apache-2.0 |
Quickly Comparing Plots by Controlling
The Dimensionality Reduction technique used
The Encoding Technique used
The dataset to be visualized
Whether to differentiate Labels or not
Set the alpha parameter
Set the metric for UMAP | visualize('t-sne','tfidf',corpus)
visualize('t-sne','count',corpus,alpha = 0.5)
visualize('t-sne','tfidf',corpus,labels =False)
visualize('umap','tfidf',corpus)
visualize('umap','tfidf',corpus,labels = False)
visualize('umap','count',corpus,metric= 'cosine') | examples/Sangarshanan/comparing_corpus_visualizers.ipynb | pdamodaran/yellowbrick | apache-2.0 |
1.3. Chemistry Scheme Scope
Is Required: TRUE Type: ENUM Cardinality: 1.N
Atmospheric domains covered by the atmospheric chemistry model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.key_properties.chemistry_scheme_scope')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "troposhere"
# "stratosphere"
# "mesosphere"
# "mesosphere"
# "whole atmosphere"
# "Other: [... | notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
1.4. Basic Approximations
Is Required: TRUE Type: STRING Cardinality: 1.1
Basic approximations made in the atmospheric chemistry model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.key_properties.basic_approximations')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
DOC.set_value("Lumped higher hydrocarbon species and oxidation products, parameterized source of Cly and Bry in stratosphere, short-lived species not advecte... | notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
1.6. Number Of Tracers
Is Required: TRUE Type: INTEGER Cardinality: 1.1
Number of advected tracers in the atmospheric chemistry model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.key_properties.number_of_tracers')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
DOC.set_value(82)
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
1.8. Coupling With Chemical Reactivity
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Atmospheric chemistry transport scheme turbulence is couple with chemical reactivity? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.key_properties.coupling_with_chemical_reactivity')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
DOC.set_value(True)
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
3. Key Properties --> Timestep Framework
Timestepping in the atmospheric chemistry model
3.1. Method
Is Required: TRUE Type: ENUM Cardinality: 1.1
Mathematical method deployed to solve the evolution of a given variable | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.method')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Operator splitting"
# "Integrated"
# "Other: [Please specify]"
DOC.set_value("Operator splitting")
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
3.2. Split Operator Advection Timestep
Is Required: FALSE Type: INTEGER Cardinality: 0.1
Timestep for chemical species advection (in seconds) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_advection_timestep')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
DOC.set_value(30)
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
3.3. Split Operator Physical Timestep
Is Required: FALSE Type: INTEGER Cardinality: 0.1
Timestep for physics (in seconds). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_physical_timestep')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
DOC.set_value(30)
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
10. Emissions Concentrations --> Surface Emissions
**
10.1. Sources
Is Required: FALSE Type: ENUM Cardinality: 0.N
Sources of the chemical species emitted at the surface that are taken into account in the emissions scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.sources')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Vegetation"
# "Soil"
# "Sea surface"
# "Anthropogenic"
# "Biomass burning"
# "... | notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
10.3. Prescribed Climatology Emitted Species
Is Required: FALSE Type: STRING Cardinality: 0.1
List of chemical species emitted at the surface and prescribed via a climatology, and the nature of the climatology (E.g. CO (monthly), C2H6 (constant)) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.prescribed_climatology_emitted_species')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
DOC.set_value("CO, CH2O, NO, C3H6, isoprene, C2H6, C2H4, C4H10, terpenes, C3H8, acetone, CH3OH, C2H5OH, H2, SO2... | notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
10.5. Interactive Emitted Species
Is Required: FALSE Type: STRING Cardinality: 0.1
List of chemical species emitted at the surface and specified via an interactive method | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.interactive_emitted_species')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
DOC.set_value("DMS")
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11. Emissions Concentrations --> Atmospheric Emissions
TO DO
11.1. Sources
Is Required: FALSE Type: ENUM Cardinality: 0.N
Sources of chemical species emitted in the atmosphere that are taken into account in the emissions scheme. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.sources')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Aircraft"
# "Biomass burning"
# "Lightning"
# "Volcanos"
# "Other: [Please specif... | notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11.3. Prescribed Climatology Emitted Species
Is Required: FALSE Type: STRING Cardinality: 0.1
List of chemical species emitted in the atmosphere and prescribed via a climatology (E.g. CO (monthly), C2H6 (constant)) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.prescribed_climatology_emitted_species')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
DOC.set_value("CO, CH2O, NO, C3H6, isoprene, C2H6, C2H4, C4H10, terpenes, C3H8, acetone, CH3OH, C2H5OH, H2,... | notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
12. Emissions Concentrations --> Concentrations
TO DO
12.1. Prescribed Lower Boundary
Is Required: FALSE Type: STRING Cardinality: 0.1
List of species prescribed at the lower boundary. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.emissions_concentrations.concentrations.prescribed_lower_boundary')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
DOC.set_value("CH4, N2O")
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.2. Species
Is Required: FALSE Type: ENUM Cardinality: 0.N
Species included in the gas phase chemistry scheme. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.species')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "HOx"
# "NOy"
# "Ox"
# "Cly"
# "HSOx"
# "Bry"
# "VOCs"
# "isoprene"
# "H2O"
# ... | notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.3. Number Of Bimolecular Reactions
Is Required: TRUE Type: INTEGER Cardinality: 1.1
The number of bi-molecular reactions in the gas phase chemistry scheme. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_bimolecular_reactions')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
DOC.set_value(157)
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.4. Number Of Termolecular Reactions
Is Required: TRUE Type: INTEGER Cardinality: 1.1
The number of ter-molecular reactions in the gas phase chemistry scheme. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_termolecular_reactions')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
DOC.set_value(21)
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.8. Number Of Steady State Species
Is Required: TRUE Type: INTEGER Cardinality: 1.1
The number of gas phase species for which the concentration is updated in the chemical solver assuming photochemical steady state | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_steady_state_species')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
DOC.set_value(19)
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.10. Wet Deposition
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Is wet deposition included? Wet deposition describes the moist processes by which gaseous species deposit themselves on solid surfaces thus decreasing their concentration in the air. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.wet_deposition')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
DOC.set_value(True)
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.11. Wet Oxidation
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Is wet oxidation included? Oxidation describes the loss of electrons or an increase in oxidation state by a molecule | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.wet_oxidation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
DOC.set_value(True)
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14.2. Gas Phase Species
Is Required: FALSE Type: ENUM Cardinality: 0.N
Gas phase species included in the stratospheric heterogeneous chemistry scheme. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.gas_phase_species')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Cly"
# "Bry"
# "NOy"
DOC.set_value("Bry")
DOC.set_value("Cly")
DOC.set_value("NOy")
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14.3. Aerosol Species
Is Required: FALSE Type: ENUM Cardinality: 0.N
Aerosol species included in the stratospheric heterogeneous chemistry scheme. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.aerosol_species')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Sulphate"
# "Polar stratospheric ice"
# "NAT (Nitric acid trihydrate)"
# "NAD (Nitric aci... | notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14.4. Number Of Steady State Species
Is Required: TRUE Type: INTEGER Cardinality: 1.1
The number of steady state species in the stratospheric heterogeneous chemistry scheme. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.number_of_steady_state_species')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
DOC.set_value(3)
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14.5. Sedimentation
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Is sedimentation is included in the stratospheric heterogeneous chemistry scheme or not? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.sedimentation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
DOC.set_value(True)
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14.6. Coagulation
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Is coagulation is included in the stratospheric heterogeneous chemistry scheme or not? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.coagulation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
DOC.set_value(True)
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15.2. Gas Phase Species
Is Required: FALSE Type: STRING Cardinality: 0.1
List of gas phase species included in the tropospheric heterogeneous chemistry scheme. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.gas_phase_species')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
DOC.set_value("3")
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15.3. Aerosol Species
Is Required: FALSE Type: ENUM Cardinality: 0.N
Aerosol species included in the tropospheric heterogeneous chemistry scheme. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.aerosol_species')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Sulphate"
# "Nitrate"
# "Sea salt"
# "Dust"
# "Ice"
# "Organic"
# "Bl... | notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15.6. Coagulation
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Is coagulation is included in the tropospheric heterogeneous chemistry scheme or not? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.coagulation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
DOC.set_value(True)
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
16.2. Number Of Reactions
Is Required: TRUE Type: INTEGER Cardinality: 1.1
The number of reactions in the photo-chemistry scheme. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.photo_chemistry.number_of_reactions')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
DOC.set_value(39)
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
17. Photo Chemistry --> Photolysis
Photolysis scheme
17.1. Method
Is Required: TRUE Type: ENUM Cardinality: 1.1
Photolysis scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmoschem.photo_chemistry.photolysis.method')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Offline (clear sky)"
# "Offline (with clouds)"
# "Online"
DOC.set_value("Offline (with clouds)")
| notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/atmoschem.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
1 Counter
A Counter is a dict subclass for counting hashable objects. It is an unordered collection where elements are stored as dictionary keys and their counts are stored as dictionary values.
1.1 construction | c1 = Counter()
c2 = Counter('gaufung')
c3 = Counter({'red':4,'blue':10})
c4 = Counter(cats=4,dogs=5) | python-statatics-tutorial/basic-theme/python-language/Collections.ipynb | gaufung/Data_Analytics_Learning_Note | mit |
1.2 using key | c = Counter(['dog', 'cat'])
c['fox'] | python-statatics-tutorial/basic-theme/python-language/Collections.ipynb | gaufung/Data_Analytics_Learning_Note | mit |
1.3 delete key
Setting a count to zero does not remove an element from a counter. Use del to remove it entirely: | c['dog'] = 0
del c['dog'] | python-statatics-tutorial/basic-theme/python-language/Collections.ipynb | gaufung/Data_Analytics_Learning_Note | mit |
1.4 elements
Return an iterator over elements repeating each as many times as its count. | c = Counter(a=4, b=2, c=0, d=-2)
print list(c.elements()) | python-statatics-tutorial/basic-theme/python-language/Collections.ipynb | gaufung/Data_Analytics_Learning_Note | mit |
1.5 most_common
Return a list of the n most common elements and their counts from the most common to the least. | Counter('abracadabra').most_common(3) | python-statatics-tutorial/basic-theme/python-language/Collections.ipynb | gaufung/Data_Analytics_Learning_Note | mit |
1.6 subtract([iterable-or-mapping])
Elements are subtracted from an iterable or from another mapping (or counter). | c = Counter(a=4, b=2, c=0, d=-2)
d = Counter(a=1, b=2, c=3, d=4)
c.subtract(d)
c | python-statatics-tutorial/basic-theme/python-language/Collections.ipynb | gaufung/Data_Analytics_Learning_Note | mit |
2 deque
Deques are a generalization of stacks and queues,Deques support thread-safe, memory efficient appends and pops from either side of the deque with approximately the same O(1) performance in either direction.
append(x)
Add x to the right side of the deque.
appendleft(x)
Add x to the left side of the deque.
... | s = [('yellow', 1), ('blue', 2), ('yellow', 3), ('blue', 4), ('red', 1)]
d = defaultdict(list)
for k, v in s:
d[k].append(v)
d.items() | python-statatics-tutorial/basic-theme/python-language/Collections.ipynb | gaufung/Data_Analytics_Learning_Note | mit |
4 namedtuple
Named tuples assign meaning to each position in a tuple and allow for more readable, self-documenting code. They can be used wherever regular tuples are used, and they add the ability to access fields by name instead of position index.
namedtuple(typename, field_names[, verbose=False][, rename=False]) | Point = namedtuple('Point', ['x', 'y'], verbose=True)
p = Point(11, y=22)
p | python-statatics-tutorial/basic-theme/python-language/Collections.ipynb | gaufung/Data_Analytics_Learning_Note | mit |
The module HARK.ConsumptionSaving.ConsIndShockModel concerns consumption-saving models with idiosyncratic shocks to (non-capital) income. All of the models assume CRRA utility with geometric discounting, no bequest motive, and income shocks are fully transitory or fully permanent.
ConsIndShockModel includes:
1. A very... | IdiosyncDict={
# Parameters shared with the perfect foresight model
"CRRA": 2.0, # Coefficient of relative risk aversion
"Rfree": 1.03, # Interest factor on assets
"DiscFac": 0.96, # Intertemporal discount factor
"LivPrb" : [0.9... | examples/ConsIndShockModel/IndShockConsumerType.ipynb | econ-ark/HARK | apache-2.0 |
The distribution of permanent income shocks is specified as mean one lognormal, with an age-varying (underlying) standard deviation. The distribution of transitory income shocks is also mean one lognormal, but with an additional point mass representing unemployment; the transitory shocks are adjusted so that the distri... | IndShockExample = IndShockConsumerType(**IdiosyncDict)
IndShockExample.cycles = 0 # Make this type have an infinite horizon
IndShockExample.solve() | examples/ConsIndShockModel/IndShockConsumerType.ipynb | econ-ark/HARK | apache-2.0 |
After solving the model, we can examine an element of this type's $\texttt{solution}$: | print(vars(IndShockExample.solution[0])) | examples/ConsIndShockModel/IndShockConsumerType.ipynb | econ-ark/HARK | apache-2.0 |
The single-period solution to an idiosyncratic shocks consumer's problem has all of the same attributes as in the perfect foresight model, with a couple additions. The solution can include the marginal marginal value of market resources function $\texttt{vPPfunc}$, but this is only constructed if $\texttt{CubicBool}$ ... | print('Consumption function for an idiosyncratic shocks consumer type:')
plot_funcs(IndShockExample.solution[0].cFunc,IndShockExample.solution[0].mNrmMin,5)
print('Marginal propensity to consume for an idiosyncratic shocks consumer type:')
plot_funcs_der(IndShockExample.solution[0].cFunc,IndShockExample.solution[0].mNr... | examples/ConsIndShockModel/IndShockConsumerType.ipynb | econ-ark/HARK | apache-2.0 |
The lower part of the consumption function is linear with a slope of 1, representing the constrained part of the consumption function where the consumer would like to consume more by borrowing-- his marginal utility of consumption exceeds the marginal value of assets-- but he is prevented from doing so by the artificia... | print('mNrmGrid for unconstrained cFunc is ',IndShockExample.solution[0].cFunc.functions[0].x_list)
print('cNrmGrid for unconstrained cFunc is ',IndShockExample.solution[0].cFunc.functions[0].y_list)
print('mNrmGrid for borrowing constrained cFunc is ',IndShockExample.solution[0].cFunc.functions[1].x_list)
print('cNrmG... | examples/ConsIndShockModel/IndShockConsumerType.ipynb | econ-ark/HARK | apache-2.0 |
The consumption function in this model is an instance of LowerEnvelope1D, a class that takes an arbitrary number of 1D interpolants as arguments to its initialization method. When called, a LowerEnvelope1D evaluates each of its component functions and returns the lowest value. Here, the two component functions are th... | plot_funcs(IndShockExample.solution[0].cFunc.functions,-0.25,5.) | examples/ConsIndShockModel/IndShockConsumerType.ipynb | econ-ark/HARK | apache-2.0 |
Simulating the idiosyncratic income shocks model
In order to generate simulated data, an instance of IndShockConsumerType needs to know how many agents there are that share these particular parameters (and are thus ex ante homogeneous), the distribution of states for newly "born" agents, and how many periods to simulat... | IndShockExample.track_vars = ['aNrm','mNrm','cNrm','pLvl']
IndShockExample.initialize_sim()
IndShockExample.simulate() | examples/ConsIndShockModel/IndShockConsumerType.ipynb | econ-ark/HARK | apache-2.0 |
We can now look at the simulated data in aggregate or at the individual consumer level. Like in the perfect foresight model, we can plot average (normalized) market resources over time, as well as average consumption: | plt.plot(np.mean(IndShockExample.history['mNrm'],axis=1))
plt.xlabel('Time')
plt.ylabel('Mean market resources')
plt.show()
plt.plot(np.mean(IndShockExample.history['cNrm'],axis=1))
plt.xlabel('Time')
plt.ylabel('Mean consumption')
plt.show() | examples/ConsIndShockModel/IndShockConsumerType.ipynb | econ-ark/HARK | apache-2.0 |
We could also plot individual consumption paths for some of the consumers-- say, the first five: | plt.plot(IndShockExample.history['cNrm'][:,0:5])
plt.xlabel('Time')
plt.ylabel('Individual consumption paths')
plt.show() | examples/ConsIndShockModel/IndShockConsumerType.ipynb | econ-ark/HARK | apache-2.0 |
Other example specifications of idiosyncratic income shocks consumers
$\texttt{IndShockConsumerType}$-- and $\texttt{HARK}$ in general-- can also represent models that are not infinite horizon.
Lifecycle example
Suppose we wanted to represent consumers with a lifecycle-- parameter values that differ by age, with a fi... | LifecycleDict={ # Click arrow to expand this fairly large parameter dictionary
# Parameters shared with the perfect foresight model
"CRRA": 2.0, # Coefficient of relative risk aversion
"Rfree": 1.03, # Interest factor on assets
"DiscFac": 0.96, ... | examples/ConsIndShockModel/IndShockConsumerType.ipynb | econ-ark/HARK | apache-2.0 |
In this case, we have specified a ten period model in which retirement happens in period $t=7$. Agents in this model are more likely to die as they age, and their permanent income drops by 30\% at retirement. Let's make and solve this lifecycle example, then look at the $\texttt{solution}$ attribute. | LifecycleExample = IndShockConsumerType(**LifecycleDict)
LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods -- a lifetime -- exactly once
LifecycleExample.solve()
print('First element of solution is',LifecycleExample.solution[0])
print('Solution has', len(LifecycleExample.solution),'elements.') | examples/ConsIndShockModel/IndShockConsumerType.ipynb | econ-ark/HARK | apache-2.0 |
This was supposed to be a ten period lifecycle model-- why does our consumer type have eleven elements in its $\texttt{solution}$? It would be more precise to say that this specification has ten non-terminal periods. The solution to the 11th and final period in the model would be the same for every set of parameters: ... | print('Consumption functions across the lifecycle:')
mMin = np.min([LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)])
LifecycleExample.unpack('cFunc') # This makes all of the cFuncs accessible in the attribute cFunc
plot_funcs(LifecycleExample.cFunc,mMin,5) | examples/ConsIndShockModel/IndShockConsumerType.ipynb | econ-ark/HARK | apache-2.0 |
"Cyclical" example
We can also model consumers who face an infinite horizon, but who do not face the same problem in every period. Consider someone who works as a ski instructor: they make most of their income for the year in the winter, and make very little money in the other three seasons.
We can represent this type... | CyclicalDict = { # Click the arrow to expand this parameter dictionary
# Parameters shared with the perfect foresight model
"CRRA": 2.0, # Coefficient of relative risk aversion
"Rfree": 1.03, # Interest factor on assets
"DiscFac": 0.96, ... | examples/ConsIndShockModel/IndShockConsumerType.ipynb | econ-ark/HARK | apache-2.0 |
This consumer type's parameter dictionary is nearly identical to the original infinite horizon type we made, except that each of the time-varying parameters now have four values, rather than just one. Most of these have the same value in each period except for $\texttt{PermGroFac}$, which varies greatly over the four ... | CyclicalExample = IndShockConsumerType(**CyclicalDict)
CyclicalExample.cycles = 0 # Make this consumer type have an infinite horizon
CyclicalExample.solve()
CyclicalExample.unpack('cFunc')
print('Quarterly consumption functions:')
mMin = min([X.mNrmMin for X in CyclicalExample.solution])
plot_funcs(CyclicalExample.cFu... | examples/ConsIndShockModel/IndShockConsumerType.ipynb | econ-ark/HARK | apache-2.0 |
A Simple Parser for Term Rewriting
This file implements a parser for terms and equations. It uses the parser generator Ply. To install Ply, change the cell below into a code cell and execute it. If the package ply is already installed, this command will only produce a message that the package is already installed.
!... | import ply.lex as lex
tokens = [ 'NUMBER', 'VAR', 'FCT', 'BACKSLASH' ] | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
The token Number specifies a natural number. Syntactically, numbers are treated a function symbols. | def t_NUMBER(t):
r'0|[1-9][0-9]*'
return t | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
Variables start with a letter, followed by letters, digits, and underscores. They must be followed by a character that is not an opening parenthesis (. | def t_VAR(t):
r'[a-zA-Z][a-zA-Z0-9_]*(?=[^(a-zA-Z0-9_])'
return t | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
Function names start with a letter, followed by letters, digits, and underscores.
They have to be followed by an opening parenthesis (. | def t_FCT(t):
r'[a-zA-Z][a-zA-Z0-9_]*(?=[(])'
return t
def t_BACKSLASH(t):
r'\\'
return t | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
Single line comments are supported and work as in C. | def t_COMMENT(t):
r'//[^\n]*'
t.lexer.lineno += t.value.count('\n')
pass | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
The arithmetic operators and a few other symbols are supported. | literals = ['+', '-', '*', '/', '\\', '%', '^', '(', ')', ';', '=', ','] | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
White space, i.e. space characters, tabulators, and carriage returns are ignored. | t_ignore = ' \t\r' | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
Syntactically, newline characters are ignored. However, we still need to keep track of them in order to know which line we are in. This information is needed later for error messages. | def t_newline(t):
r'\n'
t.lexer.lineno += 1
return | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
Given a token, the function find_colum returns the column where token starts.
This is possible, because token.lexer.lexdata stores the string that is given to the scanner and token.lexpos is the number of characters that precede token. | def find_column(token):
program = token.lexer.lexdata
line_start = program.rfind('\n', 0, token.lexpos) + 1
return (token.lexpos - line_start) + 1 | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
The function t_error is called for any token t that can not be scanned by the lexer. In this case, t.value[0] is the first character that can not be recognized by the scanner. | def t_error(t):
column = find_column(t)
print(f"Illegal character '{t.value[0]}' in line {t.lineno}, column {column}.")
t.lexer.skip(1) | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
The next assignment is necessary to make the lexer think that the code given above is part of some file. | __file__ = 'main'
lexer = lex.lex()
def test_scanner(file_name):
with open(file_name, 'r') as handle:
program = handle.read()
print(program)
lexer.input(program)
lexer.lineno = 1
return [t for t in lexer] | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
for t in test_scanner('Examples/quasigroup.eqn'):
print(t)
Specification of the Parser
We will use the following grammar to specify the language that our compiler can translate:
```
axioms
: equation
| axioms equation
;
equation
: term '=' term
;
term: term '+' term
| term ... | import ply.yacc as yacc | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
The start variable of our grammar is axioms. | start = 'axioms'
precedence = (
('nonassoc', '='),
('left', '+', '-'),
('left', '*', '/', 'BACKSLASH', '%'),
('right', '^')
)
def p_axioms_one(p):
"axioms : equation"
p[0] = ('axioms', p[1])
def p_axioms_more(p):
"axioms : axioms equation"
p[0] = p[1] + (p[2],)
def p_equation(p):... | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
Setting the optional argument write_tables to False is required to prevent an obscure bug where the parser generator tries to read an empty parse table. As we have used precedence declarations to resolve all shift/reduce conflicts, the action table should contain no conflict. | parser = yacc.yacc(write_tables=False, debug=True) | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
!cat parser.out
The notebook AST-2-Dot.ipynb provides the function tuple2dot. This function can be used to visualize the abstract syntax tree that is generated by the function yacc.parse. | %run AST-2-Dot.ipynb | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
The function parse takes a file_name as its sole argument. The file is read and parsed.
The resulting parse tree is visualized using graphviz. It is important to reset the
attribute lineno of the scanner, for otherwise error messages will not have the correct line numbers. | def test_parse(file_name):
lexer.lineno = 1
with open(file_name, 'r') as handle:
program = handle.read()
ast = yacc.parse(program)
print(ast)
return tuple2dot(ast) | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
!cat Examples/quasigroup.eqn
test_parse('Examples/quasigroup.eqn')
The function indent is used to indent the generated assembler commands by preceding them with 8 space characters. | def parse_file(file_name):
lexer.lineno = 1
with open(file_name, 'r') as handle:
program = handle.read()
AST = yacc.parse(program)
if AST:
_, *L = AST
return L
return None | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
parse_file('Examples/group-theory.eqn') | def parse_equation(s):
lexer.lineno = 1
AST = yacc.parse(s + ';')
if AST:
_, *L = AST
return L[0]
return None | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
parse_equation('i(x) * x = 1') | def parse_term(s):
lexer.lineno = 1
AST = yacc.parse(s + '= 1;')
if AST:
_, *L = AST
return L[0][1]
return None | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
parse_term('i(x) * x') | def to_str(t):
if isinstance(t, set):
return '{' + ', '.join({ f'{to_str(eq)}' for eq in t }) + '}'
if isinstance(t, list):
return '[' + ', '.join([ f'{to_str(eq)}' for eq in t ]) + ']'
if isinstance(t, dict):
return '{' + ', '.join({ f'{k}: {to_str(v)}' for k, v in t.items() }) + '}... | Python/4 Automatic Theorem Proving/Parser.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
To do:
Get temperature for Guatemala
Fetch rainfall for Afghanistan between 1980 and 1999 | url = 'http://climatedataapi.worldbank.org/climateweb/rest/v1/country/cru/tas/GTM.csv'
response = requests.get(url ) # gets the get function from the request library to find the url and put it in a loop etc
if response.status_code != 200:
print ('Failed to get data: ', response.status_code)
else:
print ('Fir... | Untitled1.ipynb | WillRhB/PythonLesssons | mit |
Using the csv library instead | import csv
with open ('test01.csv', 'r') as rawdata:
csvdata = csv.reader(rawdata)
for record in csvdata:
print (record)
url = 'http://climatedataapi.worldbank.org/climateweb/rest/v1/country/cru/tas/year/GTM.csv'
response = requests.get(url ) # gets the get function from the request library to f... | Untitled1.ipynb | WillRhB/PythonLesssons | mit |
We provide you with some helper functions to deal with images, since for this part of the assignment we're dealing with real JPEGs, not CIFAR-10 data. | def preprocess(img, size=512):
transform = T.Compose([
T.Scale(size),
T.ToTensor(),
T.Normalize(mean=SQUEEZENET_MEAN.tolist(),
std=SQUEEZENET_STD.tolist()),
T.Lambda(lambda x: x[None]),
])
return transform(img)
def deprocess(img):
transform = T.Compos... | CS231n/assignment3/StyleTransfer-PyTorch.ipynb | UltronAI/Deep-Learning | mit |
As in the last assignment, we need to set the dtype to select either the CPU or the GPU | dtype = torch.FloatTensor
# Uncomment out the following line if you're on a machine with a GPU set up for PyTorch!
# dtype = torch.cuda.FloatTensor
# Load the pre-trained SqueezeNet model.
cnn = torchvision.models.squeezenet1_1(pretrained=True).features
cnn.type(dtype)
# We don't want to train the model any further,... | CS231n/assignment3/StyleTransfer-PyTorch.ipynb | UltronAI/Deep-Learning | mit |
Computing Loss
We're going to compute the three components of our loss function now. The loss function is a weighted sum of three terms: content loss + style loss + total variation loss. You'll fill in the functions that compute these weighted terms below.
Content loss
We can generate an image that reflects the content... | def content_loss(content_weight, content_current, content_original):
"""
Compute the content loss for style transfer.
Inputs:
- content_weight: Scalar giving the weighting for the content loss.
- content_current: features of the current image; this is a PyTorch Tensor of shape
(1, C_l, H_... | CS231n/assignment3/StyleTransfer-PyTorch.ipynb | UltronAI/Deep-Learning | mit |
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