mmmayo13

import scipy.spatial.distance as dist
import numpy as np

# Prepare 2 vectors (data points) of 10 dimensions
A = np.random.uniform(0, 10, 10)
B = np.random.uniform(0, 10, 10)

print '\n2 10-dimensional vectors'
print '------------------------'
print A

mmmayo13

import os

import speech_recognition as sr

from pydub import AudioSegment
from pydub.playback import play
from gtts import gTTS as tts


def speak(text):

mmmayo13

from keras.models import Sequential
from keras.layers import Dense, Activation

dims = X_train.shape[1]
print(dims, 'dims')
print("Building model...")

nb_classes = Y_train.shape[1]
print(nb_classes, 'classes')

mmmayo13

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn import tree

# Load and split the data
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

mmmayo13

from tpot import TPOTClassifier
from sklearn.cross_validation import train_test_split
from sklearn.datasets import load_iris
import time

# Load and split the data
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

# Construct and fit TPOT classifier

mmmayo13

print mldb.put('/v1/functions/fetch', {
    "type": 'fetcher',
    "params": {}
})

print mldb.put('/v1/functions/inception', {
    "type": 'tensorflow.graph',
    "params": {
        "modelFileUrl": 'archive+'+
            'http://public.mldb.ai/models/inception_dec_2015.zip'+

mmmayo13

pd.DataFrame(runResults["confusionMatrix"])\
    .pivot_table(index="actual", columns="predicted", fill_value=0)

mmmayo13

rez = mldb.post("/v1/procedures", {
    "type": "classifier.experiment",
    "params": {
        "experimentName": "car_brand_cls",
        "inputData": """        
            SELECT 
                {* EXCLUDING(brand)} as features,
                brand as label
            FROM training_dataset
        """,

mmmayo13

print mldb.post("/v1/procedures", {
    "type": "transform",
    "params": {
        "inputData": """
            SELECT brand,
                   inception({url}) as *
            FROM images
        """,
        "outputDataset": "training_dataset"
    }

mmmayo13

mldb.query("SELECT count(*) FROM images GROUP BY brand")