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Title: Linear regression of Housing Values in Suburbs of Boston using PyData
Slug: linear-regression-with-python
Date: 2017-11-06 0:00
Category: Notebook
Tags: Python

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const hoge = results.hoge;
// ↓
const { hoge } = results;

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import android.app.Activity
import android.os.Build
import android.os.Bundle
import android.util.Log
import com.google.android.things.pio.PeripheralManagerService

class MainActivity : Activity() {
    private val TAG = "MainActivity"
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)

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from __future__ import print_function
import tensorflow as tf
class FizzBuzz():
    def __init__(self, length=30):
        self.length = length
        self.array = tf.Variable([str(i) for i in range(1, length+1)], dtype=tf.string, trainable=False)
        self.graph = tf.while_loop(self.cond, self.body, [1, self.array],
                            shape_invariants=[tf.TensorShape([]), tf.TensorShape(self.length)],
                            back_prop=False)
    def run(self):

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timesteps = 40 # 最大系列長
input_dim = 3 # 入力次元
df = pd.read_csv('example.csv')
seq = df.as_matrix()
seq_length = seq.shape[0] # 対象となるデータの系列長
seq_0padding = np.r_[seq, np.zeros((timesteps-seq_length, input_dim))]

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import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras import optimizers
from keras.callbacks import EarlyStopping
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split

# Setup dataset
np.random.seed(1234)

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import numpy as np
np.random.seed(1234)
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras import optimizers
from keras.wrappers.scikit_learn import KerasClassifier

from sklearn.utils import shuffle
from sklearn.model_selection import GridSearchCV

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import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from sklearn import svm
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score

np.random.seed(1234)
n_data = 200