def changePredict(plant_num, collectedColor, idealColor):
# Save the full data set to a 2D list
dataArray = QueryData.collectData(plant_num)
waterVals = dataArray[0]
colorVals = dataArray[1]
# Find the difference between the ideal and collected color
difference = 1 - (1.0 * collectedColor / idealColor)
newWater = waterVals[-1]
# If the difference is significant in either direction, update based on correlation
if difference > 0.05:
slope = abs(regression(waterVals, colorVals))
addAmt = slope * abs(collectedColor - idealColor)
lastWater = waterVals[-1]
newWater = lastWater + addAmt
elif difference < -0.05:
slope = abs(regression(waterVals, colorVals))
addAmt = slope * abs(collectedColor - idealColor)
lastWater = waterVals[-1]
newWater = lastWater - addAmt
healthy = True
# Remove extreme irrigation volume changes
if newWater > 30:
while newWater > 7:
newWater -= 1.1837513
healthy = False
if newWater < 2:
while newWater < 5:
newWater += 0.391
healthy = False
return newWater, healthy
def findW(plant_num, newTemp, newLight):
dataArray = QueryData.collectData(plant_num)
waterVals = dataArray[0]
colorVals = dataArray[1]
tempVals = dataArray[2]
lightVals = dataArray[3]
correlations = []
correlations.append(regression(waterVals, colorVals))
correlations.append(regression(tempVals, colorVals))
correlations.append(regression(lightVals, colorVals))
mseArray = []
wArray = []
# Use correlations to find predicted value and calculate loss accordingly
for x in range(0, len(waterVals)):
w = random.randint(0, 100)
w = float(w) / 10.0
totalArr = []
wArray.append(w)
for x in range(int(len(colorVals))):
total = correlations[0] * waterVals[x] + correlations[
1] * tempVals[x] + correlations[2] * lightVals[x]
totalArr.append(int(w * total))
mseArray.append(round(getMSE(totalArr, colorVals) / 10, 2))
# Utilize quadric regression to plot MSE values
x = Symbol('x')
A, B, C = np.polyfit(wArray, mseArray, 2)
y = A * x**2 + B * x + C
yprime = str(y.diff(x))
arr = yprime.split(' + ')
if len(arr) == 1:
arr = yprime.split(' - ')
# Parse and convert output to find ideal weight based on loss calculation
stra = str(arr[1])
strb = str(arr[0][0:-2])
converta = float(stra[0:10])
convertb = float(strb[0:10])
predictedW = float(converta) / (float(convertb))
idealCol = GetIdealColor.idealColor()
# Use weights to find theoretical predicted irrigation volume
temporary = float(idealCol) / predictedW
temporary -= (correlations[1] * newTemp + correlations[2] * newLight)
theoreticalX = temporary / correlations[0]
# Remove extreme irrigation volume changes
if theoreticalX < 0:
theoreticalX *= -1
healthy = True
if theoreticalX > 30:
while theoreticalX > 7:
theoreticalX -= 1.1837513
healthy = False
if theoreticalX < 2:
while theoreticalX < 5:
theoreticalX *= 1.23754
healthy = False
# Return theoretical irrigation volume and crop health status
return theoreticalX, healthy