kelceydamage/raspi-tasks

#!/usr/bin/env python3
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# Author: ${name=Kelcey Damage}
# Python: 3.5+
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Doc
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# {
#     'x': 'columnA',
#     'y': 'columnB',
#     'space': None,    <- linear, log, None
#     'model': 'Poly',  <- Linear, Poly
#     'd': 3
# }
# Imports
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import numpy as np
from rtl.common.task import Task
from rtl.common.regression import Models

# Globals
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# Classes
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class LinearRegression(Task):

    def __init__(self, kwargs, contents):
        super(LinearRegression, self).__init__(kwargs, contents)
        self.newColumns = [
            ('{0}{1}'.format(o['y'], o['model']), '<f8') 
            for o in self.operations
        ]
        self.addColumns()

    def lookupModel(self, modelName):
        return Models.__dict__[modelName]

    def regress(self):
        for i in range(len(self.operations)):
            o = self.operations[i]
            args = None
            if o['model'] == 'Poly':
                args = o['d']
            self.getLSpace(o['space'].encode(), self.ndata[o['x']])
            M = self.lookupModel(o['model'])(
                self.ndata[o['x']],
                self.ndata[o['y']],
                self.lSpace,
                args
            )
            self.setColumn(
                i,
                M.prediction
            )
            # temp code
            print('=> Regression[{0}] Results: m={1}, c={2}, r={3}'.format(
                o['model'],
                M.m,
                M.c,
                M.r
            ))
        return self


# Functions
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def regression(kwargs, contents):
    return LinearRegression(kwargs, contents).regress().getContents()

# Main
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