import findspark
findspark.init('/home/spark/spark-2.1.0-bin-hadoop2.7')
import pyspark
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('NuralNet').getOrCreate()
df = spark.read.json('iris.json')
df.show()
+---------------+-----------+----------+-----------+----------+-------+ |_corrupt_record|petalLength|petalWidth|sepalLength|sepalWidth|species| +---------------+-----------+----------+-----------+----------+-------+ | [| null| null| null| null| null| | null| 1.4| 0.2| 5.1| 3.5| setosa| | null| 1.4| 0.2| 4.9| 3.0| setosa| | null| 1.3| 0.2| 4.7| 3.2| setosa| | null| 1.5| 0.2| 4.6| 3.1| setosa| | null| 1.4| 0.2| 5.0| 3.6| setosa| | null| 1.7| 0.4| 5.4| 3.9| setosa| | null| 1.4| 0.3| 4.6| 3.4| setosa| | null| 1.5| 0.2| 5.0| 3.4| setosa| | null| 1.4| 0.2| 4.4| 2.9| setosa| | null| 1.5| 0.1| 4.9| 3.1| setosa| | null| 1.5| 0.2| 5.4| 3.7| setosa| | null| 1.6| 0.2| 4.8| 3.4| setosa| | null| 1.4| 0.1| 4.8| 3.0| setosa| | null| 1.1| 0.1| 4.3| 3.0| setosa| | null| 1.2| 0.2| 5.8| 4.0| setosa| | null| 1.5| 0.4| 5.7| 4.4| setosa| | null| 1.3| 0.4| 5.4| 3.9| setosa| | null| 1.4| 0.3| 5.1| 3.5| setosa| | null| 1.7| 0.3| 5.7| 3.8| setosa| +---------------+-----------+----------+-----------+----------+-------+ only showing top 20 rows
df.printSchema()
root |-- _corrupt_record: string (nullable = true) |-- petalLength: double (nullable = true) |-- petalWidth: double (nullable = true) |-- sepalLength: double (nullable = true) |-- sepalWidth: double (nullable = true) |-- species: string (nullable = true)
from pyspark.sql.types import (StructField,StringType,
IntegerType,StructType,FloatType)
df.show()
+---------------+-----------+----------+-----------+----------+-------+ |_corrupt_record|petalLength|petalWidth|sepalLength|sepalWidth|species| +---------------+-----------+----------+-----------+----------+-------+ | [| null| null| null| null| null| | null| 1.4| 0.2| 5.1| 3.5| setosa| | null| 1.4| 0.2| 4.9| 3.0| setosa| | null| 1.3| 0.2| 4.7| 3.2| setosa| | null| 1.5| 0.2| 4.6| 3.1| setosa| | null| 1.4| 0.2| 5.0| 3.6| setosa| | null| 1.7| 0.4| 5.4| 3.9| setosa| | null| 1.4| 0.3| 4.6| 3.4| setosa| | null| 1.5| 0.2| 5.0| 3.4| setosa| | null| 1.4| 0.2| 4.4| 2.9| setosa| | null| 1.5| 0.1| 4.9| 3.1| setosa| | null| 1.5| 0.2| 5.4| 3.7| setosa| | null| 1.6| 0.2| 4.8| 3.4| setosa| | null| 1.4| 0.1| 4.8| 3.0| setosa| | null| 1.1| 0.1| 4.3| 3.0| setosa| | null| 1.2| 0.2| 5.8| 4.0| setosa| | null| 1.5| 0.4| 5.7| 4.4| setosa| | null| 1.3| 0.4| 5.4| 3.9| setosa| | null| 1.4| 0.3| 5.1| 3.5| setosa| | null| 1.7| 0.3| 5.7| 3.8| setosa| +---------------+-----------+----------+-----------+----------+-------+ only showing top 20 rows
data_schema = [StructField('petalLength',FloatType(),True),
StructField('petalWidth',FloatType(),True),StructField('sepalLength',FloatType(),True),
StructField('sepalWidth',FloatType(),True),StructField('species',StringType(),True)]
Struck_Final = StructType(fields=data_schema)
Refined_Df = spark.read.json('iris.json',schema = Struck_Final)
Refined_Df.show()
+-----------+----------+-----------+----------+-------+ |petalLength|petalWidth|sepalLength|sepalWidth|species| +-----------+----------+-----------+----------+-------+ | null| null| null| null| null| | 1.4| 0.2| 5.1| 3.5| setosa| | 1.4| 0.2| 4.9| 3.0| setosa| | 1.3| 0.2| 4.7| 3.2| setosa| | 1.5| 0.2| 4.6| 3.1| setosa| | 1.4| 0.2| 5.0| 3.6| setosa| | 1.7| 0.4| 5.4| 3.9| setosa| | 1.4| 0.3| 4.6| 3.4| setosa| | 1.5| 0.2| 5.0| 3.4| setosa| | 1.4| 0.2| 4.4| 2.9| setosa| | 1.5| 0.1| 4.9| 3.1| setosa| | 1.5| 0.2| 5.4| 3.7| setosa| | 1.6| 0.2| 4.8| 3.4| setosa| | 1.4| 0.1| 4.8| 3.0| setosa| | 1.1| 0.1| 4.3| 3.0| setosa| | 1.2| 0.2| 5.8| 4.0| setosa| | 1.5| 0.4| 5.7| 4.4| setosa| | 1.3| 0.4| 5.4| 3.9| setosa| | 1.4| 0.3| 5.1| 3.5| setosa| | 1.7| 0.3| 5.7| 3.8| setosa| +-----------+----------+-----------+----------+-------+ only showing top 20 rows
Na_refine=Refined_Df.na.drop()
Na_refine.show()
+-----------+----------+-----------+----------+-------+ |petalLength|petalWidth|sepalLength|sepalWidth|species| +-----------+----------+-----------+----------+-------+ | 1.4| 0.2| 5.1| 3.5| setosa| | 1.4| 0.2| 4.9| 3.0| setosa| | 1.3| 0.2| 4.7| 3.2| setosa| | 1.5| 0.2| 4.6| 3.1| setosa| | 1.4| 0.2| 5.0| 3.6| setosa| | 1.7| 0.4| 5.4| 3.9| setosa| | 1.4| 0.3| 4.6| 3.4| setosa| | 1.5| 0.2| 5.0| 3.4| setosa| | 1.4| 0.2| 4.4| 2.9| setosa| | 1.5| 0.1| 4.9| 3.1| setosa| | 1.5| 0.2| 5.4| 3.7| setosa| | 1.6| 0.2| 4.8| 3.4| setosa| | 1.4| 0.1| 4.8| 3.0| setosa| | 1.1| 0.1| 4.3| 3.0| setosa| | 1.2| 0.2| 5.8| 4.0| setosa| | 1.5| 0.4| 5.7| 4.4| setosa| | 1.3| 0.4| 5.4| 3.9| setosa| | 1.4| 0.3| 5.1| 3.5| setosa| | 1.7| 0.3| 5.7| 3.8| setosa| | 1.5| 0.3| 5.1| 3.8| setosa| +-----------+----------+-----------+----------+-------+ only showing top 20 rows
from pyspark.ml.feature import StringIndexer
StringIndexer = StringIndexer(inputCol = "species", outputCol = "Species_category")
Na_refine.show()
StringIndexed = StringIndexer.fit(Na_refine).transform(Na_refine)
+-----------+----------+-----------+----------+-------+ |petalLength|petalWidth|sepalLength|sepalWidth|species| +-----------+----------+-----------+----------+-------+ | 1.4| 0.2| 5.1| 3.5| setosa| | 1.4| 0.2| 4.9| 3.0| setosa| | 1.3| 0.2| 4.7| 3.2| setosa| | 1.5| 0.2| 4.6| 3.1| setosa| | 1.4| 0.2| 5.0| 3.6| setosa| | 1.7| 0.4| 5.4| 3.9| setosa| | 1.4| 0.3| 4.6| 3.4| setosa| | 1.5| 0.2| 5.0| 3.4| setosa| | 1.4| 0.2| 4.4| 2.9| setosa| | 1.5| 0.1| 4.9| 3.1| setosa| | 1.5| 0.2| 5.4| 3.7| setosa| | 1.6| 0.2| 4.8| 3.4| setosa| | 1.4| 0.1| 4.8| 3.0| setosa| | 1.1| 0.1| 4.3| 3.0| setosa| | 1.2| 0.2| 5.8| 4.0| setosa| | 1.5| 0.4| 5.7| 4.4| setosa| | 1.3| 0.4| 5.4| 3.9| setosa| | 1.4| 0.3| 5.1| 3.5| setosa| | 1.7| 0.3| 5.7| 3.8| setosa| | 1.5| 0.3| 5.1| 3.8| setosa| +-----------+----------+-----------+----------+-------+ only showing top 20 rows
StringIndexed.show(10)
+-----------+----------+-----------+----------+-------+----------------+ |petalLength|petalWidth|sepalLength|sepalWidth|species|Species_category| +-----------+----------+-----------+----------+-------+----------------+ | 1.4| 0.2| 5.1| 3.5| setosa| 2.0| | 1.4| 0.2| 4.9| 3.0| setosa| 2.0| | 1.3| 0.2| 4.7| 3.2| setosa| 2.0| | 1.5| 0.2| 4.6| 3.1| setosa| 2.0| | 1.4| 0.2| 5.0| 3.6| setosa| 2.0| | 1.7| 0.4| 5.4| 3.9| setosa| 2.0| | 1.4| 0.3| 4.6| 3.4| setosa| 2.0| | 1.5| 0.2| 5.0| 3.4| setosa| 2.0| | 1.4| 0.2| 4.4| 2.9| setosa| 2.0| | 1.5| 0.1| 4.9| 3.1| setosa| 2.0| +-----------+----------+-----------+----------+-------+----------------+ only showing top 10 rows
#Df_Pandas =StringIndexed.toPandas()
#import pandas as pd
#import numpy as np
Variables = np.array(StringIndexed.select('petalLength','petalWidth','sepalLength','sepalWidth').collect())
Labels = np.array(StringIndexed.select('Species_category').collect())
Variables[0:10]
array([[1.39999998, 0.2 , 5.0999999 , 3.5 ], [1.39999998, 0.2 , 4.9000001 , 3. ], [1.29999995, 0.2 , 4.69999981, 3.20000005], [1.5 , 0.2 , 4.5999999 , 3.0999999 ], [1.39999998, 0.2 , 5. , 3.5999999 ], [1.70000005, 0.40000001, 5.4000001 , 3.9000001 ], [1.39999998, 0.30000001, 4.5999999 , 3.4000001 ], [1.5 , 0.2 , 5. , 3.4000001 ], [1.39999998, 0.2 , 4.4000001 , 2.9000001 ], [1.5 , 0.1 , 4.9000001 , 3.0999999 ]])
Labels[0:10]
array([[2.], [2.], [2.], [2.], [2.], [2.], [2.], [2.], [2.], [2.]])
import numpy as np
alphaValues = [0.01, 0.018,0.020, 2, 5, 10]
hiddenSize = 70
# Defining a sigmoid function
def sigmoid(x):
output = 1 / (1 + np.exp(-x))
return output
# derivative of sigmoid function
def sigmoidDerivativeFunction(output):
output = output * (1 - output)
return output
def forwardProp(X,weight_1,weight_2):
##Forward Propogation
layer_0 = X
layer_1 = sigmoid(np.dot(layer_0, weight_1))
layer_2 = sigmoid(np.dot(layer_1, weight_2))
return layer_0,layer_1,layer_2
def backwardProp(weight_1,weight_2,layer_1,layer_2,alpha,ForwardPropError):
#Caliculating the error value
layer_2_Sigma = ForwardPropError * sigmoidDerivativeFunction(layer_2)
layer_1_Error = layer_2_Sigma.dot(weight_2.T)
layer_1_Sigma = layer_1_Error * sigmoidDerivativeFunction(layer_1)
# We are suggesting values .. i.e how much of it is wrong in each layer
# baically updating the weights withs respective to values and the errors observed
weight_1_Update = (layer_0.T.dot(layer_1_Sigma))
weight_2_Update = (layer_1.T.dot(layer_2_Sigma))
# We are checking what direction the gradient is moving
weight_1 += alpha * weight_1_Update
weight_2 += alpha * weight_2_Update
return weight_1,weight_2
# Some values to construct the nural net work
# Later we will be using the actual data from the iris data set
X = Variables
y = Labels
for alpha in alphaValues:
print("The current alpha value = ", alpha)
np.random.seed(1)
# initializing Random weights
weight_1 = 2 * np.random.random((4, hiddenSize)) - 1
weight_2 = 2 * np.random.random((hiddenSize, 1)) - 1
# checking the weights to see how the gradiants are changing
for j in range(6000):
# Forward propogation from one layer to another i.e between layers 0 , 1, 2
# Caliculating the error value
layer_0,layer_1,layer_2 = forwardProp(X,weight_1,weight_2)
ForwardPropError = y - layer_2
if (j % 1000) == 0:
print("Error = ", np.mean(np.abs(ForwardPropError)))
weight_1,weight_2 = backwardProp(weight_1,weight_2,layer_1,layer_2,alpha,ForwardPropError)
print("weight_1", weight_1)
print("weight_2", weight_2)
The current alpha value = 0.01 Error = 0.690510991184737 Error = 0.4044019779545721 Error = 0.38845963236829706 Error = 0.38198146420821905 Error = 0.37818268565079627 Error = 0.37552260451579433 weight_1 [[-2.38709228e-01 -1.37411442e+00 -9.99364283e-01 -3.73358574e-01 -7.06141226e-01 -8.00689314e-01 -6.29554340e-01 -2.58021933e-01 -1.11310815e-01 6.18499593e-02 -1.63785872e-01 3.32507838e-01 -6.37193890e-01 9.27020008e-01 -9.49756894e-01 3.58833510e-01 1.27140570e-01 2.90955436e-01 -7.55573058e-01 -1.08704114e+00 2.63761301e-01 1.01815615e+00 -1.42180092e-01 6.83787099e-02 2.37006528e+00 8.22749523e-01 -8.29085560e-01 -9.51009415e-01 -1.76039414e+00 7.76371953e-01 -8.57485712e-01 -2.49481636e-01 1.63698978e+00 9.34282657e-02 5.67869827e-01 -3.39416962e-01 4.53993514e-02 2.20576897e+00 -9.72927629e-01 1.64560045e-01 2.20548629e+00 6.36003371e-01 -1.33883078e-02 2.09538388e+00 -7.80166777e-01 -1.88065433e-01 2.42167433e+00 -3.16396728e-01 -3.44175873e-01 -1.74543679e+00 -1.03934450e+00 -5.82247921e-01 -3.80908456e+00 -1.46032992e+00 -5.31482355e-02 -3.57847042e+00 3.96190981e-01 -8.56872696e-01 4.54306404e-02 3.30094002e-01 -6.85142737e-01 6.69720697e-01 3.52457498e-01 -1.88814918e+00 -5.85806516e-01 -3.56747740e-01 7.05813685e-01 6.13786572e-03 9.09063952e-01 1.90584487e-01] [ 1.08939265e+00 -1.44600473e+00 -7.21413335e-01 6.21080257e-01 -2.04390639e-01 -6.69306354e-01 8.54428492e-01 -7.37781014e-02 7.51605551e-01 4.47992206e-01 7.66132409e-01 2.29552616e-01 4.92072424e-01 3.37986163e-02 -4.59994605e-01 7.98636312e-01 -7.35033166e-02 9.89796074e-01 4.11765223e-01 -1.10559006e-01 -8.57536243e-01 9.06395685e-01 2.56570331e-01 4.96842327e-02 4.44267570e-01 -5.28342400e-01 8.07050401e-01 1.57379447e-01 -1.46335361e+00 2.44672042e-01 -3.83250217e-01 2.72280813e-02 1.10450040e+00 -5.61707208e-01 8.92052420e-01 3.61274562e-01 -1.05316711e+00 1.49678295e+00 3.82279570e-01 1.06444611e+00 -1.52965879e-01 -2.68303460e-01 9.94560889e-01 9.99071303e-01 -8.69617858e-01 4.84964353e-01 1.17507801e+00 1.07860487e+00 4.10810626e-01 -1.11825805e+00 -9.71152696e-01 -1.33313472e+00 -3.35764435e+00 -1.34396212e+00 7.08284164e-01 -2.47051810e+00 1.27500213e-01 6.32136042e-01 -4.75876513e-01 -4.64077336e-01 3.87991668e-01 1.81475288e+00 1.08837336e-01 -1.66764259e+00 5.53269202e-01 -6.12084503e-01 1.09174400e+00 -1.84969067e-01 7.29173731e-01 5.00367455e-01] [ 6.00832633e-02 -3.79503138e-01 -8.78319844e-01 -7.33454373e-01 -9.12637909e-01 -7.03144500e-01 -5.50709405e-01 6.21299282e-02 -2.23700948e-01 -9.96461708e-01 -8.58919290e-01 9.72673100e-01 9.47623379e-02 -8.34228375e-01 -5.23472761e-01 4.90327285e-01 -4.32079942e-01 2.64466348e-01 5.52271827e-01 3.18079110e-01 -9.18633573e-01 3.50236879e-01 -3.50260336e-03 4.37198682e-01 -6.86706065e-01 -9.17341103e-01 -8.59702824e-01 -3.67224701e-01 5.75639922e-01 9.33655293e-02 -1.38111962e-01 8.61482182e-01 -4.50752363e-01 1.99654824e-01 4.69227244e-02 3.55937369e-01 -7.11237849e-02 -6.51331967e-01 -9.30678560e-01 -8.13574890e-01 -5.25752058e-03 -8.30321586e-01 7.23447388e-01 -1.02270456e+00 -3.83427616e-01 5.06442988e-01 -8.30919373e-01 5.27682303e-02 1.27671733e-01 6.10781291e-01 -7.63810831e-01 -6.05386807e-01 2.12324681e+00 8.71913793e-01 7.78773984e-01 2.70909138e+00 -8.64346327e-01 -4.97301746e-01 3.91432459e-01 8.38279303e-01 1.01269118e+00 -4.89251170e-01 8.17544605e-01 5.60442939e-01 -2.98623100e-01 -4.55808650e-01 -1.24338441e-01 -9.37796439e-02 9.34442866e-01 8.47331552e-01] [ 1.21477642e-01 1.95151386e+00 -6.50847902e-01 -7.38442787e-01 -7.31315195e-01 6.93308839e-02 -9.57637080e-01 8.51553024e-01 7.19706329e-01 -9.79469067e-01 -6.48826272e-01 -2.91218715e-01 -7.40668255e-01 6.53381945e-01 -3.30671249e-01 8.73952030e-01 2.71465481e-01 7.49601296e-01 3.72787138e-01 8.86341673e-01 -2.12356150e-01 3.47558036e-01 5.90179848e-01 -5.15837123e-01 -8.31008384e-01 3.85311741e-01 -9.69082340e-01 -7.81638409e-02 6.80125762e-01 5.68571196e-01 -1.69447468e-01 7.34557682e-01 -6.54517448e-01 -7.49434387e-01 4.36508967e-01 9.25083855e-02 -1.02573572e+00 -1.04689923e+00 3.11741465e-01 1.35746209e+00 -1.66684627e+00 1.36703795e+00 -3.17988181e-01 -2.33520105e-02 2.78643943e-01 6.13138404e-01 -8.44537377e-01 2.81233474e-01 -4.20067909e-01 4.77513104e-01 2.99460423e-01 1.32194748e+00 3.58849067e+00 1.12611326e+00 7.15867941e-01 1.42886200e+00 6.24459241e-01 -2.04218557e-03 -1.47079340e-01 -2.01794224e-01 -7.53528800e-01 -8.59022872e-01 2.47836929e-01 9.21131257e-01 8.48511897e-01 3.01676951e-01 -1.22230984e+00 -1.84965255e-01 -2.56576111e-01 5.95159152e-01]] weight_2 [[ 1.54745971] [ 2.2253324 ] [ 0.32666674] [-0.46858446] [-0.49439822] [ 0.73452719] [ 0.04851116] [ 1.35256144] [ 1.22313531] [ 0.45749078] [ 0.0181133 ] [ 0.76763923] [-0.07753645] [ 0.80773624] [-0.29438755] [-0.61741316] [-0.70889983] [-0.57325242] [ 1.69154587] [-0.85663829] [ 0.48275509] [ 0.88999056] [ 1.18522157] [ 0.46126876] [-2.31877323] [-0.80086479] [-0.30965779] [-0.35633771] [ 1.92891734] [-0.16137188] [ 0.90118477] [ 1.08534708] [-1.50682719] [-1.54395839] [-0.41437536] [ 0.80254497] [ 0.00864407] [-2.84915122] [-0.47578897] [ 1.75774012] [-2.07579889] [ 1.20521692] [-0.17325839] [-1.77336404] [ 0.21578617] [ 0.27902669] [-2.87476872] [ 1.28409357] [-0.4793301 ] [ 1.51939484] [-0.61673377] [ 1.02007481] [-5.46193693] [-1.81097921] [ 0.82417099] [-4.14658808] [-1.02626979] [ 0.76829194] [ 1.2682596 ] [ 0.34087209] [ 1.76736861] [ 1.66901525] [ 0.8988672 ] [ 2.5218222 ] [-0.61901697] [ 0.14856633] [ 1.37259149] [-0.57921923] [ 0.82018305] [-0.68246762]] The current alpha value = 0.018 Error = 0.690510991184737 Error = 0.38999619872223495 Error = 0.3784051180020801 Error = 0.37347722443636083 Error = 0.37023572503010466 Error = 0.36761005053275975 weight_1 [[-0.12455148 -1.58890396 -0.99937084 -0.36301214 -0.70576085 -0.80278117 -0.63023666 -0.17677811 0.06853835 0.05732659 -0.16398395 0.26904893 -0.50856893 1.3929053 -0.94879402 0.36297168 0.1491929 0.30740562 -0.67569003 -1.66545693 0.2332564 1.01585272 -1.65086556 -0.07930724 2.53534016 0.79805016 -0.82870687 -0.93830456 -1.9461837 0.76151775 -0.89103005 -0.54676324 1.75941803 0.03697794 0.58222383 -0.48224787 0.02768427 2.38428692 -0.97080525 0.70650562 2.37854923 0.50754978 -0.35170899 2.22321376 -0.78506082 -0.38417249 2.59965996 -0.09437806 -0.33284991 -2.13975255 -1.04184054 -0.64039182 -5.11107402 -2.3403337 -0.08921126 -4.37463114 0.39791705 -0.88500131 0.06062011 0.03650246 -0.67486226 1.02598405 0.31337275 -2.08326827 -0.76502946 -0.56844184 1.26346232 -0.05984496 0.90951873 0.19407636] [ 1.17665822 -1.550962 -0.72142573 0.62366291 -0.20426226 -0.67061828 0.85427195 0.08277302 0.96208849 0.44714083 0.76617808 0.20835204 0.52127111 0.49895992 -0.4595528 0.79984654 -0.10978132 0.99403901 0.48830558 -0.74907212 -0.86673934 0.90455195 -0.55672457 0.09611399 0.51888469 -0.52976928 0.80715754 0.16165718 -1.54360928 0.23954389 -0.3941364 -0.0490149 1.15598594 -0.66235627 0.89518096 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2.52489827 1.48007834 0.73376262 3.98395 -0.94710695 -0.54038216 0.38889654 0.54283049 1.12056131 -0.68216506 0.77480028 0.60777374 -0.61202711 -0.42838634 -0.40982951 0.02306434 0.94211242 0.85171932] [ 0.20243548 2.0873482 -0.65073942 -0.73255937 -0.73127252 0.07513766 -0.95802623 1.00844015 0.85244825 -0.98255066 -0.64903931 -0.31457 -0.69694379 0.5132531 -0.33183818 0.87560424 0.34324517 0.75496798 0.37450186 1.477352 -0.22653659 0.35910154 1.07690464 -0.77756292 -0.95086527 0.47783466 -0.96891964 -0.08045953 0.81455346 0.56147384 -0.17462133 0.57134574 -0.74439185 -1.00716439 0.43695962 -0.00578798 -1.02948404 -1.17545864 0.30950557 1.53464183 -1.77948374 1.35834141 -0.58154072 -0.12513524 0.29055193 0.55423337 -0.97644124 0.30459399 -0.48892175 0.7619867 0.29116737 1.36345466 5.17741075 1.70579375 0.69686868 0.29159675 0.77683117 -0.01651186 -0.16706766 -0.39277721 -0.95407437 -1.1422919 0.23033183 1.06071147 0.55334693 0.39406852 -1.77492671 -0.25131039 -0.25065524 0.59676986]] weight_2 [[ 1.37570633] [ 2.49970995] [ 0.3267667 ] [-0.47394733] [-0.49446892] [ 0.73668087] [ 0.04477001] [ 1.5671165 ] [ 1.26254651] [ 0.45453968] [ 0.01058302] [ 0.85773978] [-0.29049267] [ 1.08229175] [-0.2934387 ] [-0.53402081] [-0.98764257] [-0.47492804] [ 1.82619268] [-1.42510747] [ 0.472205 ] [ 0.96748945] [ 1.45636843] [ 1.17249587] [-2.50136043] [-0.98177565] [-0.30989471] [-0.35705908] [ 2.18555887] [-0.06177532] [ 0.89618531] [ 1.26402588] [-1.58862285] [-1.68108106] [-0.29840931] [-0.76352585] [-0.03859773] [-3.11973891] [-0.47472283] [ 1.95417895] [-2.27690686] [-1.30179176] [ 0.9002734 ] [-1.83925356] [ 0.2327029 ] [ 0.82698343] [-3.13528946] [ 1.13972089] [-0.74338359] [ 2.24095248] [-0.61274984] [ 1.01903401] [-5.27224132] [-2.26491029] [ 0.91240829] [-4.11831957] [-1.22006144] [ 0.75840214] [ 1.28245819] [ 0.63499415] [ 1.85047612] [ 1.45940554] [ 0.98596129] [ 2.8075598 ] [-0.26420209] [ 0.37977139] [ 1.63200434] [-0.99607922] [ 0.89928705] [-0.59949751]] The current alpha value = 0.02 Error = 0.690510991184737 Error = 0.39038990342665403 Error = 0.37793097287620514 Error = 0.3728308348996243 Error = 0.3693056836247568 Error = 0.366315738781636 weight_1 [[-5.34825766e-02 -1.62306682e+00 -9.99381757e-01 -3.60015153e-01 -7.05639053e-01 -8.03967265e-01 -6.30391380e-01 -1.64581543e-01 1.29697258e-01 5.61832807e-02 -1.63992546e-01 2.27473279e-01 -4.70694583e-01 1.52796245e+00 -9.48386616e-01 3.63792404e-01 1.58203847e-01 3.10404897e-01 -6.66307168e-01 -1.79573418e+00 2.25194104e-01 1.01436620e+00 -1.73965367e+00 -6.37850336e-02 2.56116237e+00 7.96371904e-01 -8.28599353e-01 -9.34067613e-01 -1.97808801e+00 7.53226700e-01 -8.97283681e-01 -5.32430588e-01 1.77406614e+00 2.74159372e-02 5.82829890e-01 -4.98322647e-01 2.51113809e-02 2.41785350e+00 -9.69859274e-01 1.01082905e+00 2.40465248e+00 7.37978429e-02 -3.38594046e-01 2.23850602e+00 -7.87422142e-01 -3.69910571e-01 2.63232764e+00 1.81774185e-02 -3.32186538e-01 -2.20918660e+00 -1.04200263e+00 -6.28799432e-01 -5.35564865e+00 -2.59902096e+00 -1.04734328e-01 -4.62191178e+00 4.05614515e-01 -8.90412431e-01 5.88300930e-02 1.15058083e-02 -6.85045135e-01 -6.37434118e-01 2.95541851e-01 -2.11887423e+00 -8.09641737e-01 -5.97237243e-01 1.43550399e+00 -6.88292346e-02 9.09166102e-01 1.94844720e-01] [ 1.27191541e+00 -1.56674805e+00 -7.21430513e-01 6.24373220e-01 -2.04224273e-01 -6.71046723e-01 8.54240470e-01 1.45954442e-01 1.05694527e+00 4.46937386e-01 7.66201174e-01 1.94850985e-01 5.18614158e-01 6.76808602e-01 -4.59410958e-01 8.00058136e-01 -1.43194581e-01 9.94718511e-01 5.42122431e-01 -9.46811038e-01 -8.69553505e-01 9.03916111e-01 -5.98891003e-01 1.70748300e-01 5.29503347e-01 -5.48246334e-01 8.07187087e-01 1.62882898e-01 -1.55577696e+00 2.36832064e-01 -3.95300923e-01 -8.55860416e-03 1.16036163e+00 -7.25798326e-01 8.94846053e-01 -4.47800323e-03 -1.06190480e+00 1.58817519e+00 3.83501325e-01 1.78163045e+00 -6.78188518e-02 -8.03614446e-01 9.05627605e-01 1.05954579e+00 -8.73387701e-01 4.91154416e-01 1.26733062e+00 1.40483484e+00 2.99076309e-01 -1.32717998e+00 -9.71287391e-01 -1.35457515e+00 -4.28288520e+00 -2.37850114e+00 6.92580237e-01 -3.64211743e+00 7.18498846e-02 6.26267245e-01 -3.44524466e-01 -5.10616786e-01 4.33288298e-01 9.78019776e-01 9.12816082e-02 -1.76778624e+00 6.54555445e-01 -7.37564506e-01 1.71596524e+00 -4.40623026e-01 7.28573806e-01 5.01512402e-01] [-1.09048496e-01 -3.00543812e-01 -8.78171768e-01 -7.15556443e-01 -9.12240256e-01 -6.97327923e-01 -5.51901444e-01 -1.23572465e-01 -5.30652052e-01 -1.00538282e+00 -8.59503447e-01 8.71470209e-01 2.95414069e-01 -1.40383791e+00 -5.24196871e-01 4.96381149e-01 -5.13629539e-01 2.87942687e-01 5.06901785e-01 7.29785383e-01 -9.65605279e-01 3.64376897e-01 1.49554480e-01 6.39334324e-01 -7.35848203e-01 -9.79296092e-01 -8.59129817e-01 -3.59005579e-01 6.32452575e-01 6.81620652e-02 -1.81740551e-01 4.23531082e-01 -4.84048377e-01 3.99803128e-01 6.36395666e-02 1.57095370e-01 -8.37399432e-02 -7.02127508e-01 -9.31841407e-01 -1.65773522e+00 -5.51725927e-02 -1.03248923e+00 5.86871742e-01 -1.06037744e+00 -3.72318089e-01 1.46154265e-01 -8.81701220e-01 -2.50676001e-01 1.65104136e-01 7.43897489e-01 -7.76880690e-01 -6.03796197e-01 2.36828970e+00 1.63103479e+00 7.14281198e-01 4.33993252e+00 -9.75418351e-01 -5.51512752e-01 3.95315717e-01 5.81012325e-01 1.16217871e+00 -5.54248395e-01 7.54491963e-01 6.19298147e-01 -6.58717402e-01 -4.24899106e-01 -4.63936986e-01 4.15584948e-02 9.42965439e-01 8.52781085e-01] [ 1.32000126e-01 2.11257300e+00 -6.50727772e-01 -7.30707357e-01 -7.31226767e-01 7.53580488e-02 -9.58129367e-01 1.01373916e+00 8.34223346e-01 -9.83404611e-01 -6.49079332e-01 -3.34296727e-01 -6.96284869e-01 4.63290547e-01 -3.31871330e-01 8.76093065e-01 3.78885685e-01 7.55898826e-01 3.34071402e-01 1.66337551e+00 -2.29995338e-01 3.60572037e-01 1.13792064e+00 -8.42600730e-01 -9.71038187e-01 5.26703130e-01 -9.68862100e-01 -7.92128672e-02 8.38361858e-01 5.57925572e-01 -1.82509909e-01 5.84454677e-01 -7.56975546e-01 -1.03184810e+00 4.36529422e-01 6.82792699e-04 -1.02897093e+00 -1.20094370e+00 3.09526545e-01 1.33975646e+00 -1.80078464e+00 8.73251097e-01 -6.84456175e-01 -1.37485129e-01 2.90566463e-01 5.70403147e-01 -1.00158546e+00 2.11787087e-01 -4.96276346e-01 8.13129371e-01 2.90025036e-01 1.35846378e+00 6.00621368e+00 1.97079801e+00 6.88310256e-01 1.29247906e-01 8.34718656e-01 -2.17255841e-02 -1.91509557e-01 -4.43486191e-01 -1.04245058e+00 -4.78370222e-01 2.21277131e-01 1.08699210e+00 5.37160880e-01 4.06759659e-01 -2.04459741e+00 -2.55205504e-01 -2.49873500e-01 5.97250631e-01]] weight_2 [[ 1.35046711] [ 2.5313671 ] [ 0.32677711] [-0.47556013] [-0.49450915] [ 0.73669019] [ 0.04377796] [ 1.58767721] [ 1.26211303] [ 0.45376176] [ 0.0085168 ] [ 0.88642889] [-0.37986385] [ 1.17539446] [-0.29347541] [-0.51204079] [-1.04551185] [-0.44886611] [ 1.85212385] [-1.54045768] [ 0.47000046] [ 0.98815412] [ 1.58124014] [ 1.19829969] [-2.51187564] [-1.05254457] [-0.30997136] [-0.35880925] [ 2.21800465] [-0.03229408] [ 0.89277996] [ 1.36865688] [-1.57817709] [-1.70373515] [-0.26488216] [-0.80946424] [-0.05028275] [-3.15153109] [-0.4748451 ] [ 1.96890833] [-2.29334466] [-0.01720525] [ 1.03195364] [-1.82785645] [ 0.23240966] [ 0.96187318] [-3.16451288] [ 1.10191654] [-0.7759782 ] [ 2.36902205] [-0.61220637] [ 0.99452238] [-5.43756064] [-2.41356482] [ 0.9367433 ] [-4.29458446] [-1.27070963] [ 0.75545353] [ 1.28486855] [ 0.79074057] [ 1.89316799] [ 0.60445696] [ 1.01020398] [ 2.84619744] [-0.28777324] [ 0.40127619] [ 1.7913628 ] [-1.04794288] [ 0.92026215] [-0.57765006]] The current alpha value = 2 Error = 0.690510991184737 Error = 0.6666666666685086 Error = 0.6666666666685086 Error = 0.6666666666685086 Error = 0.6666666666685086 Error = 0.6666666666685087 weight_1 [[-2.01248173 -1.10477751 -0.99902637 -0.39406892 -0.70755648 -0.78512739 -0.6275964 -0.65197946 -0.36009098 0.07373175 -0.16226664 0.42563106 -0.62724055 0.86617724 -0.96108829 0.33232028 -0.19770212 0.10734464 -0.84821185 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3.18598359 0.45096578 0.82898125 3.43594848 -1.11199895 -0.17403559 0.7498051 0.92052539 -0.45944124 -0.07271185 0.89172467 0.95778293 -1.8525195 -0.25937636 1.63174092 0.24385103 1.01714909 0.80840509] [ 1.27569039 2.7675345 -0.65025691 -0.75443985 -0.73284411 0.08729193 -0.95602113 0.75039179 0.67860032 -0.97000032 -0.64722651 -0.17338595 -0.63423304 0.44785921 -0.35110687 0.84673311 -0.05483118 0.49376188 0.6374609 0.88261503 0.06268601 1.81765362 0.29668963 -0.23709543 0.50296217 0.2036277 -0.96998362 -1.93416808 0.62026091 0.48830434 -0.16775027 0.78952211 -0.3336229 -0.79157521 0.1954564 0.22400034 -0.58131449 -0.04034269 0.23837499 1.63913915 -1.45045434 1.19792921 -1.12810437 -0.6698362 0.23678818 0.65509865 -0.47367213 0.41570925 -0.31119601 -0.2650291 -0.70652241 1.80568981 1.64156815 0.36031351 0.73971827 0.13805369 0.20907708 0.16612562 1.0468066 -0.1615585 -0.57928172 0.79310842 0.28792709 1.25504173 -0.75749276 0.60873082 0.67435637 0.2708426 -0.19862278 0.57336927]] weight_2 [[ 0.65420729] [ 2.31820074] [ 0.32715664] [-0.45552674] [-0.4934391 ] [ 0.74164738] [ 0.05990923] [ 2.0110594 ] [ 1.48148085] [ 0.46569933] [ 0.03960756] [ 1.75190782] [ 0.34146649] [-0.17156699] [-0.27608163] [ 0.42329884] [ 0.02124632] [ 0.36904035] [ 2.28760451] [ 0.71120827] [ 0.54017138] [ 1.2681521 ] [ 2.20709285] [ 1.00749617] [-1.27390535] [-0.17122367] [-0.30865623] [ 1.09709043] [ 2.05807825] [ 0.82254877] [ 0.90488453] [ 2.11679876] [ 0.49993446] [-0.74939313] [ 0.49882885] [ 1.36794214] [-0.1296569 ] [-2.68965559] [-0.43381202] [ 1.64506467] [-2.40378922] [ 1.71165764] [ 0.26155063] [-0.81173359] [ 0.29581677] [ 1.23880488] [-2.55360822] [ 1.46918956] [ 0.47109171] [ 2.31593797] [-0.16558411] [ 1.24168767] [ 2.09631983] [ 1.82184694] [ 1.86359081] [ 1.26314204] [-0.25275195] [ 0.8488457 ] [ 1.39314325] [ 1.31244073] [ 2.40372321] [ 0.41119566] [ 1.93143181] [ 2.72023344] [ 2.6861149 ] [ 1.86857245] [-1.08405813] [ 0.82053554] [ 1.84067335] [ 0.36062875]] The current alpha value = 5 Error = 0.690510991184737 Error = 0.6666666666666666 Error = 0.6666666666666666 Error = 0.6666666666666666 Error = 0.6666666666666666 Error = 0.6666666666666666 weight_1 [[-4.78227001e+00 -3.42291698e+00 -9.97909045e-01 -3.92170015e-01 -7.09158879e-01 -7.39834247e-01 -6.27771622e-01 -1.16662975e+00 -5.90528969e-01 6.78791733e-02 -1.63250152e-01 5.08419142e-01 -6.81458050e-01 1.03109067e+00 -9.84883516e-01 3.19398161e-01 -2.46169705e-01 9.22922150e-02 -1.04169008e+00 -1.72759307e-01 -1.89601402e+00 2.59533640e+00 -2.71129071e+00 5.30575498e-01 5.36872682e+00 9.55934536e-01 -8.30528420e-01 -2.49242878e+00 -1.20391549e-01 6.57053645e-01 -6.19635072e-01 -1.74114942e-01 4.41101823e-01 8.56861349e-01 1.48071808e-01 -3.66670829e-01 -3.04619764e+00 5.19134906e+00 -1.07811545e+00 9.71197395e-01 1.09361198e+00 -1.11061229e+00 1.22319638e+00 5.15806206e+00 -7.66593062e-01 -1.01094716e-01 5.02911291e+00 -1.31411924e+00 -5.82552442e-01 -2.81494701e+00 -2.17136580e+00 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[ 1.01329468] [ 4.06309106] [ 1.97848167] [-0.70615665] [ 2.16818437] [ 3.42110349] [-1.00989925] [-5.84872157] [-0.32863072] [ 3.05764676] [-5.75701907] [ 3.92908893] [ 1.45287834] [-0.83523523] [ 0.69197088] [ 3.16558995] [-5.34203476] [ 3.30755556] [ 1.04569904] [ 5.32743287] [ 0.65240312] [ 2.34963645] [ 6.07465288] [ 4.49656231] [ 3.80308967] [ 4.59086454] [-0.10496726] [ 1.00334717] [ 2.44872945] [ 3.16578076] [ 4.90948321] [-0.32142883] [ 3.84935925] [ 5.73823745] [ 7.91905705] [ 4.21751813] [-3.32069556] [ 1.72061662] [ 3.70283831] [ 2.29785893]] The current alpha value = 10 Error = 0.690510991184737 Error = 0.6666666666666666 Error = 0.6666666666666666 Error = 0.6666666666666666 Error = 0.6666666666666666 Error = 0.6666666666666666 weight_1 [[-9.39858402e+00 -7.28648295e+00 -9.96046840e-01 -3.89005176e-01 -7.11829540e-01 -6.64345684e-01 -6.28063667e-01 -2.02438096e+00 -9.74592886e-01 5.81248785e-02 -1.64889334e-01 6.46399282e-01 -7.71820599e-01 1.30594646e+00 -1.02454222e+00 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6.25468034] [ 9.08574987] [ -1.54246964] [ 7.04590451] [ 10.76824401] [ 16.6406273 ] [ 8.13242759] [ -7.04842509] [ 3.22075161] [ 6.80644609] [ 5.52657543]]
DataFrame[Species_category: double]
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